diff --git a/docs/index.rst b/docs/index.rst
index 7493179b..4279682a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -42,17 +42,7 @@ Getting started
    plt.plot(v.T)  # Plot voltage trace.
 
 
-If you want to learn more, we have tutorials on how to:
-
-- `simulate morphologically detailed neurons <notebooks/tutorials/01_morph_neurons.html>`_
-- `simulate networks of such neurons <notebooks/tutorials/02_small_network.html>`_
-- `set parameters of cells and networks <notebooks/tutorials/03_setting_parameters.html>`_
-- `speed up simulations with GPUs and jit <notebooks/tutorials/04_jit_and_vmap.html>`_
-- `define your own channels and synapses <notebooks/tutorials/05_channel_and_synapse_models.html>`_
-- `define groups <notebooks/tutorials/06_groups.html>`_
-- `read and handle SWC files <notebooks/tutorials/08_importing_morphologies.html>`_
-- `compute the gradient and train biophysical models <notebooks/tutorials/07_gradient_descent.html>`_
-
+If you want to learn more, check out our [Tutorials](https://jaxley.readthedocs.io/en/latest/tutorials.html), [FAQ](https://jaxley.readthedocs.io/en/latest/faq.html), or [Advanced tutorials](https://jaxley.readthedocs.io/en/latest/advanced_tutorials.html).
 
 
 Installation
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index c75b879e..2c980c66 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -8,7 +8,6 @@ Tutorials
 
    tutorials/01_morph_neurons.ipynb
    tutorials/02_small_network.ipynb
-   tutorials/03_setting_parameters.ipynb
    tutorials/04_jit_and_vmap.ipynb
    tutorials/05_channel_and_synapse_models.ipynb
    tutorials/07_gradient_descent.ipynb
diff --git a/docs/tutorials/00_jaxley_api.ipynb b/docs/tutorials/00_jaxley_api.ipynb
index a362e91d..2a4d0744 100644
--- a/docs/tutorials/00_jaxley_api.ipynb
+++ b/docs/tutorials/00_jaxley_api.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "285dd123",
+   "id": "da2c9396",
    "metadata": {},
    "source": [
     "# Key concepts in Jaxley"
@@ -10,13 +10,13 @@
   },
   {
    "cell_type": "markdown",
-   "id": "503f6a2f",
+   "id": "85159dff",
    "metadata": {},
    "source": [
     "In this tutorial, we will introduce you to the basic concepts of Jaxley.\n",
     "You will learn about:\n",
     "\n",
-    "- Modules\n",
+    "- Modules (e.g., Cell, Network,...)\n",
     "    - nodes\n",
     "    - edges\n",
     "- Views\n",
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "863cbf6a",
+   "id": "a02d1cba",
    "metadata": {},
    "source": [
     "First, we import the relevant libraries:"
@@ -71,7 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "cafa5416",
+   "id": "254ae5b7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,10 +89,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e66ed93d",
+   "id": "e4609897",
    "metadata": {},
    "source": [
-    "# Modules\n",
+    "## Modules\n",
     "\n",
     "In Jaxley, we heavily rely on the concept of Modules to build biophyiscal models of neural systems at various scales.\n",
     "Jaxley implements four types of Modules:\n",
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5f30473",
+   "id": "3b0c8c99",
    "metadata": {},
    "source": [
     "`Compartment`s are the atoms of biophysical models in Jaxley. All mechanisms and synaptic connections live on the level of `Compartment`s and can already be simulated using `jx.integrate` on their own. Everything you do in Jaxley starts with a `Compartment`."
@@ -115,7 +115,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "46df1e0d",
+   "id": "e7bb6ff0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b95db1d3",
+   "id": "2ecfcba1",
    "metadata": {},
    "source": [
     "Mutliple `Compartments` can be connected together to form longer, linear segments / cables, which we call `Branch`es and are equivalent to sections in `NEURON`."
@@ -133,7 +133,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "47931540",
+   "id": "9f98976c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65a391cb",
+   "id": "b105d02f",
    "metadata": {},
    "source": [
     "In order to construct cell morphologies in Jaxley, multiple `Branches` can to be connected together as a `Cell`:"
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "1d3f000d",
+   "id": "72970335",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0393f5f1",
+   "id": "39bcca94",
    "metadata": {},
    "source": [
     "Finally, several `Cell`s can be grouped together to form a `Network`, which can than be connected together using `Synpase`s."
@@ -173,7 +173,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "57f1d06a",
+   "id": "4991db7b",
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0759474",
+   "id": "39620690",
    "metadata": {},
    "source": [
     "Every module tracks information about its current state and parameters in two Dataframes called `nodes` and `edges`.\n",
@@ -208,7 +208,7 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "fe66ae91",
+   "id": "5588cc6e",
    "metadata": {},
    "outputs": [
     {
@@ -957,7 +957,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "bf0dc575",
+   "id": "bf1d3eab",
    "metadata": {},
    "outputs": [
     {
@@ -1012,15 +1012,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dc728a1",
+   "id": "4fd24f54",
    "metadata": {},
    "source": [
-    "# Views"
+    "## Views"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "27eb65c5",
+   "id": "bed93c1f",
    "metadata": {},
    "source": [
     "Since these `Module`s can become very complex, Jaxley utilizes so called `View`s to make working with `Module`s easy and intuitive. \n",
@@ -1031,7 +1031,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "f62e5dbe",
+   "id": "7f6eb6bc",
    "metadata": {},
    "outputs": [
     {
@@ -1051,7 +1051,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fc17a31",
+   "id": "84ba470e",
    "metadata": {},
    "source": [
     "Views behave very similarly to `Module`s, i.e. the `cell(0)` (the 0th cell of the network) behaves like the `cell` we instantiated earlier. As such, `cell(0)` also has a `nodes` attribute, which keeps track of it's part of the network:"
@@ -1060,7 +1060,7 @@
   {
    "cell_type": "code",
    "execution_count": 67,
-   "id": "8ec00de5",
+   "id": "0fa2447d",
    "metadata": {},
    "outputs": [
     {
@@ -1472,7 +1472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31592de0",
+   "id": "f1613a0c",
    "metadata": {},
    "source": [
     "Let's use `View`s to visualize only parts of the `Network`. Before we do that, we create x, y, and z coordinates for the `Network`:"
@@ -1481,7 +1481,7 @@
   {
    "cell_type": "code",
    "execution_count": 68,
-   "id": "e9bca1c4",
+   "id": "1bb9588f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1494,7 +1494,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ac6f1b9",
+   "id": "df46f5b9",
    "metadata": {},
    "source": [
     "We can now visualize the entire `net` (i.e., the entire `Module`) with the `.vis()` method..."
@@ -1503,7 +1503,7 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "id": "caa11808",
+   "id": "518e0adf",
    "metadata": {},
    "outputs": [
     {
@@ -1535,7 +1535,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dcbf134",
+   "id": "51b8c856",
    "metadata": {},
    "source": [
     "...but we can also create a `View` to visualize only parts of the `net`:"
@@ -1544,7 +1544,7 @@
   {
    "cell_type": "code",
    "execution_count": 70,
-   "id": "839f8784",
+   "id": "f68e1872",
    "metadata": {},
    "outputs": [
     {
@@ -1580,7 +1580,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78de7a09",
+   "id": "01f17b79",
    "metadata": {},
    "source": [
     "### How to create `View`s"
@@ -1588,7 +1588,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e61338e",
+   "id": "e51d4801",
    "metadata": {},
    "source": [
     "Above, we used `net.cell(0)` to generate a `View` of the 0-eth cell. `Jaxley` supports many ways of performing such indexing:"
@@ -1597,7 +1597,7 @@
   {
    "cell_type": "code",
    "execution_count": 71,
-   "id": "cd700a93",
+   "id": "db4e06a3",
    "metadata": {},
    "outputs": [
     {
@@ -1626,7 +1626,7 @@
   {
    "cell_type": "code",
    "execution_count": 72,
-   "id": "a58db1a0",
+   "id": "83375a2a",
    "metadata": {},
    "outputs": [
     {
@@ -2039,7 +2039,7 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "id": "15e6a146",
+   "id": "4352fe0b",
    "metadata": {},
    "outputs": [
     {
@@ -2059,7 +2059,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bda74acf",
+   "id": "172134e9",
    "metadata": {},
    "source": [
     "_Note: In case you need even more flexibility in how you select parts of a Module, Jaxley provides a `select` method, to give full control over the exact parts of the `nodes` and `edges` that are part of a `View`. On examples of how this can be used, see [Advanced Indexing](https://jaxleyverse.github.io/jaxley/latest/tutorial/09_advanced_indexing/) and [Advanced Parameter Sharing](https://jaxleyverse.github.io/jaxley/latest/tutorial/10_advanced_parameter_sharing/)._"
@@ -2067,7 +2067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8139ea0c",
+   "id": "58fe5518",
    "metadata": {},
    "source": [
     "You can also iterate over networks, cells, and branches:"
@@ -2076,7 +2076,7 @@
   {
    "cell_type": "code",
    "execution_count": 79,
-   "id": "a575dc3c",
+   "id": "96cbde44",
    "metadata": {},
    "outputs": [
     {
@@ -2166,7 +2166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e61755db",
+   "id": "bf6c0923",
    "metadata": {},
    "source": [
     "Finally, you can also use `View`s in a context manager:"
@@ -2175,7 +2175,7 @@
   {
    "cell_type": "code",
    "execution_count": 80,
-   "id": "93f32db8",
+   "id": "c643cdff",
    "metadata": {},
    "outputs": [
     {
@@ -2258,15 +2258,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c405d9be",
+   "id": "b5f570d6",
    "metadata": {},
    "source": [
-    "# Channels"
+    "## Channels"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "332dafd4",
+   "id": "4fe61ee4",
    "metadata": {},
    "source": [
     "The `Module`s that we have created above will not do anything interesting, since by default Jaxley initializes them without any mechanisms in the membrane. To change this, we have to insert channels into the membrane. For this purpose `Jaxley` implements `Channel`s that can be inserted into any compartment using the `insert` method of a `Module` or a `View`:"
@@ -2275,7 +2275,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "7b4310c9",
+   "id": "79b7346b",
    "metadata": {},
    "outputs": [
     {
@@ -2472,7 +2472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1f9aee6",
+   "id": "69abe63c",
    "metadata": {},
    "source": [
     "This is also were `View`s come in handy, as it allows to easily target the insertion of channels to specific compartments."
@@ -2481,7 +2481,7 @@
   {
    "cell_type": "code",
    "execution_count": 77,
-   "id": "509f38e0",
+   "id": "5f71dbc3",
    "metadata": {},
    "outputs": [
     {
@@ -2557,15 +2557,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9934927d",
+   "id": "6335a45c",
    "metadata": {},
    "source": [
-    "# Synapses"
+    "## Synapses"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a93346a6",
+   "id": "cd788406",
    "metadata": {},
    "source": [
     "To connect different cells together, Jaxley implements a `connect` method, that can be used to couple 2 compartments together using a `Synapse`. Synapses in Jaxley work only on the compartment level, that means to be able to connect two cells, you need to specify the exact compartments on a given cell to make the connections between. Below is an example of this:"
@@ -2574,7 +2574,7 @@
   {
    "cell_type": "code",
    "execution_count": 78,
-   "id": "dbb10724",
+   "id": "26b4a1ad",
    "metadata": {},
    "outputs": [
     {
@@ -2663,7 +2663,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61faa5c5",
+   "id": "8271833a",
    "metadata": {},
    "source": [
     "As you can see above, now the `edges` dataframe is also updated with the information of the newly added synapse. "
@@ -2671,7 +2671,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9693d0e3",
+   "id": "4ec6b0ce",
    "metadata": {},
    "source": [
     "Congrats! You should now have an intuitive understand of how to use Jaxley's API to construct, navigate and manipulate neuron models."
diff --git a/docs/tutorials/01_morph_neurons.ipynb b/docs/tutorials/01_morph_neurons.ipynb
index 892a5eb3..9d7012d4 100644
--- a/docs/tutorials/01_morph_neurons.ipynb
+++ b/docs/tutorials/01_morph_neurons.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e32c96c4",
+   "id": "37919d74",
    "metadata": {},
    "source": [
     "# Basics of Jaxley"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a5ed7b3",
+   "id": "0188c129",
    "metadata": {},
    "source": [
     "In this tutorial, you will learn how to:\n",
@@ -38,6 +38,9 @@
     "cell.branch(0).insert(Na())\n",
     "cell.branch(0).insert(K())\n",
     "\n",
+    "# Change parameters.\n",
+    "cell.set(\"axial_resistivity\", 200.0)\n",
+    "\n",
     "# Visualize the morphology.\n",
     "cell.compute_xyz()\n",
     "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
@@ -58,7 +61,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf258599",
+   "id": "38268f5f",
    "metadata": {},
    "source": [
     "First, we import the relevant libraries:"
@@ -66,8 +69,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "520ccc1e",
+   "execution_count": 1,
+   "id": "4b9c410c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd9ebcaf",
+   "id": "1a0b2e6e",
    "metadata": {},
    "source": [
     "We will now build our first cell in `Jaxley`. You have two options to do this: you can either build a cell bottom-up by defining the morphology yourselve, or you can [load cells from SWC files](https://jaxleyverse.github.io/jaxley/latest/tutorial/00_jaxley_api.ipynb/).\n"
@@ -96,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "721b28e3",
+   "id": "a8ce0e48",
    "metadata": {},
    "source": [
     "### Define the cell from scratch\n",
@@ -106,8 +109,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "2045c204",
+   "execution_count": 2,
+   "id": "7b7570a6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +120,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7174009",
+   "id": "5c7c3146",
    "metadata": {},
    "source": [
     "Next, we can assemble branches into a cell. To do so, we have to define for each branch what its parent branch is. A `-1` entry means that this branch does not have a parent."
@@ -125,8 +128,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "36b22f57",
+   "execution_count": 3,
+   "id": "9886b5b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,15 +139,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd133c02",
+   "id": "24987966",
    "metadata": {},
    "source": [
-    "To learn more about `Compartment`s, `Branch`es, and `Cell`s, see [this tutorial](More backgrond on compartments, branches, as and indexing like `cell.branch(0)` is in [this tutorial](https://jaxleyverse.github.io/jaxley/latest/tutorial/00_jaxley_api.ipynb/)."
+    "To learn more about `Compartment`s, `Branch`es, and `Cell`s, see [this tutorial](http://localhost:8000/tutorials/00_jaxley_api.html)."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dd7de7ed",
+   "id": "ddf32ecc",
    "metadata": {},
    "source": [
     "### Read the cell from an SWC file\n",
@@ -158,7 +161,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04b2f9f6",
+   "id": "50b0ee53",
    "metadata": {},
    "source": [
     "### Visualize the cells"
@@ -166,7 +169,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d417e50",
+   "id": "d12a997a",
    "metadata": {},
    "source": [
     "Cells can be visualized as follows:"
@@ -174,8 +177,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "26161684",
+   "execution_count": 4,
+   "id": "7ace82c5",
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e02e3f6d",
+   "id": "886c03fb",
    "metadata": {},
    "source": [
     "### Insert mechanisms\n",
@@ -208,8 +211,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "a43f666c",
+   "execution_count": 5,
+   "id": "482842f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -220,7 +223,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc80bbd5",
+   "id": "372cdbbc",
    "metadata": {},
    "source": [
     "Once the cell is created, we can inspect its `.nodes` attribute which lists all properties of the cell:"
@@ -228,8 +231,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "a64d27e7",
+   "execution_count": 6,
+   "id": "ace66b7e",
    "metadata": {},
    "outputs": [
     {
@@ -574,7 +577,7 @@
        "[10 rows x 25 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,16 +588,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17c3f945",
+   "id": "6ee2aa54",
    "metadata": {},
    "source": [
-    "You can also inspect just parts of the `cell`, for example its 1st branch:"
+    "Note that `Jaxley` uses the same units as the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html). You can also inspect just parts of the `cell`, for example its 1st branch:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "79e54e48",
+   "execution_count": 7,
+   "id": "99486ffe",
    "metadata": {},
    "outputs": [
     {
@@ -715,7 +718,7 @@
        "[2 rows x 25 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,7 +729,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f2715e8e",
+   "id": "df9d8914",
    "metadata": {},
    "source": [
     "The easiest way to know which branch is the 1st branch (or, e.g., the zero-eth compartment of the 1st branch) is to plot it in a different color:"
@@ -734,8 +737,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "adfe9b88",
+   "execution_count": 8,
+   "id": "93e05448",
    "metadata": {},
    "outputs": [
     {
@@ -758,7 +761,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ed619f3",
+   "id": "37068682",
    "metadata": {},
    "source": [
     "More background and features on indexing as `cell.branch(0)` is in [this tutorial](https://jaxleyverse.github.io/jaxley/latest/tutorial/00_jaxley_api.ipynb/)."
@@ -766,7 +769,187 @@
   },
   {
    "cell_type": "markdown",
-   "id": "addd0f81",
+   "id": "f186714a",
+   "metadata": {},
+   "source": [
+    "### Change parameters of the cell\n",
+    "\n",
+    "You can change properties of the cell with the `.set()` method (more details in [this tutorial](http://localhost:8000/tutorials/03_setting_parameters.html)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7698b918",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell.branch(1).set(\"axial_resistivity\", 200.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed74f56",
+   "metadata": {},
+   "source": [
+    "And we can again inspect the `.nodes` to make sure that the axial resistivity indeed changed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5923c7d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>local_cell_index</th>\n",
+       "      <th>local_branch_index</th>\n",
+       "      <th>local_comp_index</th>\n",
+       "      <th>length</th>\n",
+       "      <th>radius</th>\n",
+       "      <th>axial_resistivity</th>\n",
+       "      <th>capacitance</th>\n",
+       "      <th>v</th>\n",
+       "      <th>Leak</th>\n",
+       "      <th>Leak_gLeak</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Na_m</th>\n",
+       "      <th>Na_h</th>\n",
+       "      <th>K</th>\n",
+       "      <th>K_gK</th>\n",
+       "      <th>eK</th>\n",
+       "      <th>K_n</th>\n",
+       "      <th>global_cell_index</th>\n",
+       "      <th>global_branch_index</th>\n",
+       "      <th>global_comp_index</th>\n",
+       "      <th>controlled_by_param</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-70.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-70.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   local_cell_index  local_branch_index  local_comp_index  length  radius  \\\n",
+       "2                 0                   0                 0    10.0     1.0   \n",
+       "3                 0                   0                 1    10.0     1.0   \n",
+       "\n",
+       "   axial_resistivity  capacitance     v  Leak  Leak_gLeak  ...  Na_m  Na_h  \\\n",
+       "2              200.0          1.0 -70.0  True      0.0001  ...   NaN   NaN   \n",
+       "3              200.0          1.0 -70.0  True      0.0001  ...   NaN   NaN   \n",
+       "\n",
+       "       K  K_gK  eK  K_n  global_cell_index  global_branch_index  \\\n",
+       "2  False   NaN NaN  NaN                  0                    1   \n",
+       "3  False   NaN NaN  NaN                  0                    1   \n",
+       "\n",
+       "   global_comp_index  controlled_by_param  \n",
+       "2                  2                    1  \n",
+       "3                  3                    1  \n",
+       "\n",
+       "[2 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cell.branch(1).nodes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ace103",
+   "metadata": {},
+   "source": [
+    "In a similar way, you can modify channel properties or initial states (units are [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7d19f8c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cell.branch(0).set(\"K_gK\", 0.01)  # modify potassium conductance.\n",
+    "cell.set(\"v\", -65.0)  # modify initial voltage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc42dc3c",
    "metadata": {},
    "source": [
     "### Stimulate the cell\n",
@@ -776,13 +959,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "id": "acb04249",
+   "execution_count": 12,
+   "id": "bee2e483",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -795,7 +978,7 @@
     "dt = 0.025\n",
     "t_max = 10.0\n",
     "time_vec = np.arange(0, t_max+dt, dt)\n",
-    "current = jx.step_current(i_delay=1.0, i_dur=1.0, i_amp=0.1, delta_t=dt, t_max=t_max)\n",
+    "current = jx.step_current(i_delay=1.0, i_dur=2.0, i_amp=0.08, delta_t=dt, t_max=t_max)\n",
     "\n",
     "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n",
     "_ = plt.plot(time_vec, current)"
@@ -803,7 +986,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4243feae",
+   "id": "6fa300d5",
    "metadata": {},
    "source": [
     "We then stimulate one of the compartments of the cell with this step current:"
@@ -811,8 +994,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "id": "c672f42f",
+   "execution_count": 13,
+   "id": "d98c6b29",
    "metadata": {},
    "outputs": [
     {
@@ -830,7 +1013,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c2a5d05",
+   "id": "7812af98",
    "metadata": {},
    "source": [
     "### Define recordings"
@@ -838,7 +1021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c8fc290",
+   "id": "0f6b64f2",
    "metadata": {},
    "source": [
     "Next, you have to define where to record the voltage. In this case, we will record the voltage at two locations:"
@@ -846,8 +1029,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "id": "22f0ba99",
+   "execution_count": 14,
+   "id": "0960cbb2",
    "metadata": {},
    "outputs": [
     {
@@ -867,7 +1050,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad2b612f",
+   "id": "7fa4e61d",
    "metadata": {},
    "source": [
     "We can again visualize these locations to understand where we inserted recordings:"
@@ -875,13 +1058,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "id": "5d496bdc",
+   "execution_count": 15,
+   "id": "82d152ab",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -893,13 +1076,13 @@
    "source": [
     "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n",
     "_ = cell.vis(ax=ax)\n",
-    "_ = cell.branch(0).loc(0.0).vis(ax=ax, col=\"b\")\n",
-    "_ = cell.branch(3).loc(1.0).vis(ax=ax, col=\"g\")"
+    "_ = cell.branch(0).loc(0.0).vis(ax=ax, col=\"b\", type=\"scatter\")\n",
+    "_ = cell.branch(3).loc(1.0).vis(ax=ax, col=\"g\", type=\"scatter\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6fc954c2",
+   "id": "ce1667e9",
    "metadata": {},
    "source": [
     "### Simulate the cell response\n",
@@ -909,8 +1092,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "3f2b6e72",
+   "execution_count": 16,
+   "id": "fa4e5721",
    "metadata": {},
    "outputs": [
     {
@@ -928,23 +1111,23 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7505714",
+   "id": "b1081b09",
    "metadata": {},
    "source": [
-    "The `jx.integrate` function returns an array of shape `(num_recordings, num_timepoints). In our case, we inserted `2` recordings and we simulated for 10ms at a 0.025 time step, which leads to 402 time steps.\n",
+    "The `jx.integrate` function returns an array of shape `(num_recordings, num_timepoints)`. In our case, we inserted `2` recordings and we simulated for 10ms at a 0.025 time step, which leads to 402 time steps.\n",
     "\n",
     "We can now visualize the voltage response:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "id": "1da5c31a",
+   "execution_count": 17,
+   "id": "52379ce4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -961,7 +1144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cb35db9",
+   "id": "2d73e1cd",
    "metadata": {},
    "source": [
     "At the location of the first recording (in blue) the cell spiked, whereas at the second recording, it did not. This makes sense because we only inserted sodium and potassium channels into the first branch, but not in the entire cell."
@@ -969,19 +1152,11 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc95bb4f",
+   "id": "11f48baa",
    "metadata": {},
    "source": [
-    "Congrats! You have just run your first morphologically detailed neuron simulation in `Jaxley`. We suggest to continue by learning how to [build networks](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html). If you are only interested in single cell simulations, you can directly jump to learning how to [modify parameters of your simulation](https://jaxley.readthedocs.io/en/latest/tutorials/03_setting_parameters.html). If you want to simulate detailed morphologies from SWC files, checkout our tutorial on [working with detailed morphologies](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html)."
+    "Congrats! You have just run your first morphologically detailed neuron simulation in `Jaxley`. We suggest to continue by learning how to [build networks](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html). If you are only interested in single cell simulations, you can directly jump to learning how to [speed up simulations](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). If you want to simulate detailed morphologies from SWC files, checkout our tutorial on [working with detailed morphologies](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html)."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "49241f33",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/tutorials/02_small_network.ipynb b/docs/tutorials/02_small_network.ipynb
index 8e8370a9..72b18413 100644
--- a/docs/tutorials/02_small_network.ipynb
+++ b/docs/tutorials/02_small_network.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "45dd6e5d",
+   "id": "90a14a0a",
    "metadata": {},
    "source": [
     "# Network simulations in Jaxley"
@@ -10,13 +10,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5d0d590",
+   "id": "8a6ee5d1",
    "metadata": {},
    "source": [
     "In this tutorial, you will learn how to:\n",
     "\n",
     "- connect neurons into a network  \n",
     "- visualize networks  \n",
+    "- use the `.edges` attribute to inspect and change synaptic parameters\n",
     "\n",
     "Here is a code snippet which you will learn to understand in this tutorial:\n",
     "```python\n",
@@ -35,6 +36,9 @@
     "    IonotropicSynapse(),\n",
     ")\n",
     "\n",
+    "# Change synaptic parameters.\n",
+    "net.select(edges=[0, 1]).set(\"IonotropicSynapse_gS\", 0.1)  # nS\n",
+    "\n",
     "# Visualize the network.\n",
     "net.compute_xyz()\n",
     "fig, ax = plt.subplots(1, 1, figsize=(4, 4))\n",
@@ -44,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3adb22be",
+   "id": "8c67dec8",
    "metadata": {},
    "source": [
     "In the previous tutorial, you learned how to build single cells with morphological detail, how to insert stimuli and recordings, and how to run a first simulation. In this tutorial, we will define networks of multiple cells and connect them with synapses. Let's get started:"
@@ -52,8 +56,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
-   "id": "26976fd9",
+   "execution_count": 1,
+   "id": "d053f37a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -74,7 +78,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b065ebbc",
+   "id": "0f67fdb1",
    "metadata": {},
    "source": [
     "### Define the network\n",
@@ -84,8 +88,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
-   "id": "17ec4fb3",
+   "execution_count": 2,
+   "id": "8dbea2ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "85366241",
+   "id": "3e021130",
    "metadata": {},
    "source": [
     "We can assemble multiple cells into a network by using `jx.Network`, which takes a list of `jx.Cell`s. Here, we assemble 11 cells into a network:"
@@ -104,8 +108,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
-   "id": "ffc6a25e",
+   "execution_count": 3,
+   "id": "275a0617",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +119,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0bdbbed5",
+   "id": "2a8d8beb",
    "metadata": {},
    "source": [
     "At this point, we can already visualize this network:"
@@ -123,13 +127,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
-   "id": "e06cf68e",
+   "execution_count": 4,
+   "id": "aa7c7cff",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 300x600 with 1 Axes>"
       ]
@@ -147,15 +151,15 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b0bf783",
+   "id": "dd9582ce",
    "metadata": {},
    "source": [
-    "Note: you can use `move_to` to have more control over the location of cells, e.g.: `network.cell(i).move_to(x=0, y=200)`"
+    "Note: you can use `move_to` to have more control over the location of cells, e.g.: `network.cell(i).move_to(x=0, y=200)`."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0e866383",
+   "id": "fb251c0c",
    "metadata": {},
    "source": [
     "As you can see, the neurons are not connected yet. Let's fix this by connecting neurons with synapses. We will build a network consisting of two layers: 10 neurons in the input layer and 1 neuron in the output layer.\n",
@@ -165,19 +169,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
-   "id": "8f629229",
+   "execution_count": 5,
+   "id": "02f19e14",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/michaeldeistler/Documents/phd/jaxley/jaxley/modules/base.py:1533: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
-      "  self.pointer.edges = pd.concat(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pre = net.cell(range(10))\n",
     "post = net.cell(10)\n",
@@ -186,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "83bc510d",
+   "id": "8fa9123f",
    "metadata": {},
    "source": [
     "Let's visualize this again:"
@@ -194,13 +189,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
-   "id": "a6e16ba3",
+   "execution_count": 6,
+   "id": "f6c78f17",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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1YO5c9v7jj4Hbt4W1h2P/cOGyY6pWrYqUlBSsW7dOs0xUqVR466234OPjgzt37ghroAV5/31WWPbFC2DQIODfrUYOp0j4HpcD0b9/f3z//fd6xyQSCc6cOWOV/a/z5xVYs8YHs2ffh7u7VknCwsKsMt4//wCRkUBmJrB4MTBqlMWH4NghfHPeAKVFuADg+fPn8PX1tYGzqgjAZQA1AcwA8Inmk7i4OKs96Vy6lAmWiwtw4QJghEcJx8Hhm/OlnLFjx0KpVNrIw57ABAsAPgLgVUxbyzF8ONC6NZt1DRzIl4ycoil9z9dLIb///jt69+6N3NxczTF3d3esXbsWYWFhVhtXrQZefz0bN254YMiQ6xgzhiWNt+aYYjHw3XdA7drAX38BixYBH3xgteE4joolUq/aO46auvnhw4cUEBBAYNMfAkASiYSWLFliMxu2bGEpl11diR4/ttmw9M03bFxnZ6KXMnlzShl2V8maYzqtWrVCYGCgXixj7969oVKpMHLkSJvZ0b07UK8ee9o3f77NhsXQoUBsLJCdDQwYwBxVOZwCuHDZGbNmzYJYLMahQ4c0xypXroyMjAz8IkBxQpFIm8lh6VLAQPFwq4z77bcsFOjYMWDhQtuMy3EMuHDp4OTkBJFIpIlTdHFxgZ+fHyIjI9GrVy8sWLAAN2/etMrYhw8fhkKhwMyZMzWZKFxcXHD06FH8888/cHNzs8q4xtCxI0v+l5WldRS1BSEhWsGaOhW4etV2Y3PsG+4OoUNJs0Popqvx9PRESEgIateujTZt2qBLly5GhRClpqYiKipKU927oN/PPvsMEydOLJE91mTvXuC11wCZDLh5EwgOts24RECnTsCuXUDDhsDffwOlMGSzTMP9uAxg7IVZu3Yt9u7di6tXr+Lhw4dIT083O7spoJ+b3s/PD1WrVkV0dDR+/fVXXLx4Ua9tu3btsHv3bpPHshZEQKtWwOHDwHvvAd98Y7uxExOBiAggLY3N+OxIzzkWgAuXASzlgKpSqXD8+HHs2LEDZ8+exe3bt5GcnIwXL14gLy9PkxLHFMqXL4+4uDh4enqa3Ie1OXwYaNmSzXhu3AAqVbLd2OvWsU16mYzlq4+IsN3YHOvCHVBtQFZWFnJycqBSqZCfn6+ZjZmj/3/++ScSExPtWrQAln7mtddY9tJPP7Xt2P37s+IaublMwHTSlnHKIHzGpcPKlSuxb98+XL9+HUlJSUhPT0dubi7yzXwWX5AlVbeMWeXKlfHPP/9g9uzZDpUo8MQJtlEvkbDN8mrVbDf2gwdspvXsGRPOqVNtNzbHevClogFssTnv7e2NkJAQ1KlTB6+99hpiY2PtIr+XNejSBfjjD6BvX+CHH2w79vr1wH//ywrLnjoFREXZdnyO5eHCZQBjL4yTk5NmdlUwS1IqlQgICEBYWBhiYmLQo0cPhIaG2shy++TsWeaUKhKxoq61atlubCKgVy9g61aWr/7ECbbvxXFcuHAZoDRlh7AXevUCtmwB3ngD+Pln24796BErcfb0KTBjBjBzpm3H51gWvjnPsRmzZrEZ16ZNLP2MLfH3Z178ADBnDpsBcsoWXLg4JlG7NtCnD3s/fbrtx+/TB3j9dfaEc8AAVuKMU3bgwsUxmZkzWRqa335jG+W2RCQCli0DfH1ZWTNbu2dwhIULF8dkatRgTxYBYWZdvr7A8uXs/bx5thdPjnBw4eKYxYwZzKdr1y5WG9HW9O4NvPkmS3szYABLg8Mp/XDh4phFlSosxTIATJsmjA1LlrAN+ytX+BPGsgIXLo7ZTJ3KHEL37wcOHrT9+OXKaQvJLlgAHD9uexs4toULF8dsKlYEhgxh76dNY06itqZ7d+ZRr1azJWNWlu1t4NgOLlwcizB5MiCXswIXe/YIY8PXXwOBgcD168ItWzm2gQsXxyKUL89KiwHCzbq8vYGVK9n7r75iSQc5pRMuXByLMXEiK+R68iTw++/C2NClC1sqErGfmZnC2MGxLly4OBbD359VoQaYX5dQxVwXLmQzwJs32RKWU/rgwsWxKOPGAW5uwPnzLAhbCDw9gdWr2fuvvwZ0CiZxSglcuDgWxccH+PBD9n7GDOHqIXboALz7Lns/aBDw/LkwdnCsAxcujsX56CM267l82fYpb3T58ktWjejWLV5go7TBhUsHlUoltAmlAk9PYOxY9n7mTJbBQQiUSuC779j7pUuZgyyndMCFSwe5XK4pCCuRSODs7Ixy5cqhRo0a6NSpE6ZPn45TPJLXKMaMYR7tN26wdMtCERsLDBvG3g8aBGRkCGcLx3LwDKg6mJJzXiwWw9nZGR4eHihfvjxq1aqFVq1aoUePHnZftcfazJ8PTJgAVK4MXLvGwoKEICMDiIwE7tyxfU1IzqsxKUMx2Yi5c+cSABozZozmWFZWFo0YMYK8vb3J1dWVevXqRUlJSXrn3b17lzp16kQKhYJ8fX3p448/pry8vBKNnZaWRgAoLS2t2HabNm2iwYMHU+PGjalChQrk5uZGTk5OJBKJCIDJL7FYTHK5nLy8vKhatWrUrl07GjBgADVp0oSOHj1aou/iSDx/TuTnRwQQrVwprC379zM7AKLdu4W1haOPsfenLjYRrpMnT1JoaChFRkbqCdewYcMoODiY9u3bR6dPn6bGjRtTkyZNNJ+rVCqKiIig2NhYOnfuHO3YsYN8fHxo0qRJJRrflAtjiAsXLtAnn3xC3bp1o1q1apGPjw85OzuTWCw2Wdg2b95stl32ysKFTCyCg4mys4W15f33tbakpgprC0eLXQpXRkYGVatWjfbs2UMtW7bUCFdqaipJpVLatGmTpu3Vq1cJAB07doyIiHbs2EFisVhvFrZ8+XJSKpWUk5NjtA2WEq6srCzavHkzDR8+nJo3b04VK1YkpVJJUqnU7FmZr68vPXz40Cz77JGsLKKgICYYixcLa8vz50RVqjBbBg8W1haOFrsUrv79+9MHH3xARKQnXPv27SMA9OzZM732ISEh9NVXXxER0bRp0ygqKkrv81u3bhEAOnv2rMExs7OzKS0tTfNKSEgw6sIsWrSIevToQeHh4RaZSRW8JBIJubq6UkBAAEVHR9Pbb79Ny5cvp5YtWxZq26xZsxIvhe2dpUuZWAQGEmVmCmvL4cNEIhGzZ8cOYW3hMOxOuDZs2EARERGUlZVFRPrCtX79epLJZIXOadCgAY0fP56IiIYMGULt2rXT+/zFixcEgHYU81c3Y8aMIgXkVRfGlL0rmUymt3c1adIkOnLkiNHik5WVRVWrVtXrVyQSlXg5bM9kZxNVrMjE4ssvhbaG6MMPmS1BQUQpKUJbwzFFuKzmDpGQkIAxY8Zg/fr1Nq/oPGnSJKSlpWleCQkJRp0nkUgAQOMO4erqiqCgIDRo0AD9+vXD6tWr8eTJExATfOTn5yMnJwcpKSm4ceMGdu/ejc8++wzNmjWDk5OTUWM6OzsjPj4eJ0+ehKurKwCAiDB37lwoFArsLwXOR3K5Ns3MvHnCe7HPng1Urw48eKD18uc4GNZS0S1btmiWSQUv/DubkEgktHfvXqstFV/Gkpvz1uaLL74otDytUKFCoevkaOTmaveX5s4V2hqio0eJxGJmz2+/CW1N2caulorp6el06dIlvVf9+vXpv//9L126dEmzOf/LL79ozrl27VqRm/OPHj3StFmxYgUplUrKLsEjKkcSrgI6depUaGnasWNHoc0yi//7PyYU3t5E9vBPMW4csycggOjpU6GtKbvYlXAVhe4eFxFzhwgJCaH9+/fT6dOnKSYmhmJiYjSfF7hDtGvXjs6fP0+7du0iX19fQd0hbMmzZ88oODi40L7a/PnzhTbNJFQqoho1mFjMmiW0NeyJZ4E9ffsKbU3ZxZT706ae861atUKdOnXwv//9DwCQnZ2NsWPHYsOGDcjJyUH79u2xbNkyBAQEaM65e/cuhg8fjoMHD8LV1RXvvPMO5s2bZ/QeEmCiZ64dcfjwYbRv3x7ZOrW3XFxc8N133yEiIsKqYxOx4qu6hIWFlej66/LTT6ycmFIJ3L7NspYKycmTQEwMyx32669Az57C2lMWsWvPeSFx1BnXy0ybNs1sf7GSvVoTsIOAcnrH4+LiTP4O+flEtWuzWc7kyRa8OGYwaRKzx8+P6MkToa0pe9jVU0WO5fnkk0+QkpICuVxug9GcAKwG0BHAGQD1LNKrWAzMmsXef/018OSJRbo1ixkzgPBw4PFjbQZXjn3Dg6wdiDfeeAO//PKL3jEnJyecP3/eKuPdvCnHmDHBuHtXDplMjSlTHqJ371SzlooAW37Wrw+cPQt8/DGrhSg0Z84AjRqxxIc//wy88YbQFpUd+FLRAI6+VFy+fLnGnaTgFRAQYJMQodRUou7dtQHKQ4ZYJubwjz9YfwoFkb1EOk2bxmzy8SHSeZDNsTJ2/1RRKBxVuOLi4kipVOoJlkwmo+3bt9vUjvx8ojlztKEyDRoQ3btnXp9qNVHjxqy/0aMtY6e55OQQRUYym3r3ZjZyrA8XLgM4mnBlZWVRrVq1CoUB6bqSCMGuXcwHq2BWsm+fef3t2cP6ksmIEhIsY6O5nDtH5OTE7NqwQWhrygZ8c95MkpOThTYBgwYNgkKhwJUrVzTHGjRogNzcXI0biVC0b8/2gurWBZKTgddeY8kCTd0lbdsWaNECyM0F5syxrK2mUqeONjxp5EggKUlQcziGsKKQ2g3GKrquq4FIJCInJydyc3OjChUqUMOGDWngwIG0bt06q4TfrF+/npycnPRmWd7e3nT79m2Lj2UumZlEAwZo97169yZKTzetr0OHWB9SKZG9fNXcXKK6dZld3bvzJaO14UtFAxh7YXRFw9hXQXZTb29vql69OnXs2JEmT55MR48eNSpDRHx8PHl7e+v16eTkROvXr7fU17cKajXR8uVMcACimjWJrl41ra/YWNbHoEGWtdEcLl7UfrfvvxfamtINFy4DGHthCrKbdunShWrWrEnlypWzSE6ugsByV1dXCgoKovr161OPHj3Iw8OjUNvBDpbh7tgxovLl2Q3u7k5kSjLXY8fY+RIJ0Y0blrfRVObMYXZ5ehLdvy+0NaUXLlwGsOTmfEZGBm3YsIGGDRtGTZo0oZCQEHJ3dzc7N31ERITDJhBMSiJq1Uq7dJwwgaikX6VTJ/uLGczLI6pfn9nVuTNfMloLu49VFApLOaBev34dW7ZswYkTJ3Dz5k08evQIGRkZyM3NhVqtNrnfuLg4hIeHm3y+PaBSsaKrX37Jfm/bFtiwAfD1Ne78M2eYU6pIBMTFAbVqWc/WknDlCnsYkZsLrFkDDBggtEWlD1PuTy5cOowbNw5Hjx5FYmIinj17hqysLOTn58OcSyQSiSCVSuHi4oJy5cohNDQUdevWRaNGjXDv3j30798fPj4+Jvdvb/z8M6tf+OIFqyK9eTPQoIFx5/bqBWzZwrzWhayA/TIFZdaUSladu0IFoS0qXXDhMoA16irq1lRUKpUIDAxEzZo10aJFC3Tr1k0vw0VZ4/JlJkI3bgAyGasi/e67rz7v0iUgKootOM+fZ+/tgfx8oFkz4Phx5hKyc2fhjBkc0+HCZQBjL4xMJkNeXh7EYjGkUilcXV3h4+ODSpUqoV69eujYsSMaN25sVpxeWSEtDXjnHWDbNvb7u+8CixcDr8ri/eabLPVN9+7A1q1WN9Norl9nPl7Z2cCqVcYJMcc4uHAZoLQEWTsaajXLMT91qjawevNmICTE8DnXrrFMDWo1cOoUO8de+OorYOxYwN2dzQ4rVhTaotKBKfcn95znWA2xGJg8Gdi1iyUMPH0aqFcP2LfP8Dk1agB9+7L306fbxk5jGTMGaNoUyMgABg82PWKAYz5cuDhWp1079tQwOpqFCrVrB3z+ueEbf/p0QCJhe0lHj9rW1uKQSNiTRYWCie+KFUJbVHbhwsWxCaGhwF9/AQMHsmXgxInA668D6emF21atqnU7KIgbtBeqVWPLX4DlErt9W1h7yipcuDg2Q6EAvv0W+OYbQCplOd4bNQKuXi3cdto01mb/fuDgQZubWiyjRrHg8BcvmOuHGS58HBPhwsWxKSIR8N57wJEjQPnybDO+YUO2aa9LxYraJ3fTptnXfpJYzJaMrq5MVJctE9qisgcXLo4gNGrEUje3asUqW7/+OjB+PPPAL2DKFFYF+6+/gD17BDO1SCpXZo6pAHNOvXlTWHvKGly4OILh58cE6eOP2e8LFjAHz4ICGuXLA8OHs/f2NusCgGHDgDZtgMxMtieXny+0RWUHLlwcQXFyYoL1889s6bV/P3OZOHWKfT5xIuDiwuof/vGHsLa+jFjM9uzc3IC//wYWLRLaorIDFy6OXfDGG0ycqlcHEhJYiM2qVYC/v7Zk2PTp9jfrCg3VBpZPnsw87DnWhwsXx26oVYvNtHr2ZNkYhg5lG/Tvv89mNefOsSBse2PIEJbGOjubLxltBRcujl2hVLInjHPnapdiPXsy/y+AzbrsTRhEImanUskCsb/6SmiLSj9cuDh2h0jE9rZ27QLKlWOhQj/8wPbALl+2r5Q3BQQHAwsXsvfTprE8XhzrwYWLY7e89hoLFapXD3j2jD29A4CZM/XdJuyFgQOBjh2BnBy2ZLRHG0sLXLg4dk3FisyPa9Ag7cb8jRvA6tXC2lUUIhF7oODhwfbqFiwQ2qLSCxcujt3j7MyEasUKFugMAKNHAxcvCmtXUZQvr3WLmDGDpb/hWB4uXDrcuXMHKj6/t0tEIvaUcd8+tmmfl8dChX75RWjLCtOvH9C1K7NxwAD2k2NZuHDpULlyZUilUohEIohEIjg5OcHV1RWBgYGoV68e3nrrLSxduhRJvLyxYLRsyfa4ALaX9MYbhUOFhEYkYrNDLy8W1lSQTYJjOXgGVB1KknNe95wCgfP29kbFihVRt25dvPbaa4iNjeVpnq1AdjZQpQrw4IH2WOvWwMaNLIzIXvjxR5YU0cmJ7XnVqSO0RfYJT91sAGMvzM2bN7Ft2zYcO3YMN27cQFJSEp4/f47c3Fzkm+k8JJFIIJPJ4O7ujoCAAMhkMty7dw+ff/45BvCaVyVm2TJg5Eg2q8nNZSlmKlRgPmANGwptHYMI6N2bOc1GRbHIAJlMaKvsD5NSq1uysKO9YsmCsHl5ebR9+3YaM2YMtWzZkipVqkQeHh4klUpNLgi7cOFC879kGSM7mygkRFuANiyMvZfJiFassJ/irUlJROXKMdumTxfaGvuEV7I2gKWE6+TJkzRt2jTq2LEjhYWFkbe3N8nlchKLxSZXsC54ubm50ZkzZyz0jcsGq1YxQfD1Jbp/n6hnT2017UGDiLKyhLaQ8dNPzCaJhIj/ExeGC5cBjL0wo0ePpgYNGlBQUBC5urqSRCIxeRZV8BKLxeTs7EzlypWjsLAw6tSpE82cOZPOnDlDH3zwQaH21atXpyx7uePsnNxcosqVmSjMnctmWfPmEYnF7Fj9+kR37ghtJeONN5hNERFstsjRwoXLAMZemJIIkkgkIicnJ3Jzc6Pg4GCKiYmhIUOG0IYNGygjI8No2/Ly8qhx48aF+u/bt6+5X7tMsG4dEwRvb6KCf949e7TLs3Ll2O9C8/gxmxkCRJMnC22NfcGFywDGXhiZTKY3S/Lx8aHw8HDq0aMHzZ49m+Li4qxmY0JCAvn4+OiJl0QioTVr1lhtzNKASkVUowYThFmztMfv3CGqV48dF4u1MzIh+eUXrT0nTwpriz3BhcsAltyctzabNm0iqVSqJ2Cenp4UHx8vtGl2y8aNTBA8PIhSUrTHs7KIBg/W7nv17KmdlQnFW28xW2rWtJ89OKEx5f7kDqh2xuuvv47c3FwMGzZMcyw1NRXVqlVDnTp1uGd/EbzxBlC7NpCWpk3qB2hDhVauZG4IW7YwVwkhMzcsXsySI169ykKCOCZiRSG1GxxpxqVLXl4eRUVFFdr/Gj58uNCm2R2//spmMm5uRE+eFP78xAmiChVYG1dXop9/tr2NBWzdql0yHj0qnB32gin3J3dAdQCuX7+Oxo0bIzU1VXNMKpVi/vz5aN26tVW98/PztYHNBYSFhdldRAARUL8+C7EZN05bgUeXJ0+AN99kee0BVqRj7lzm2W5r+vcHvv+epao+f57VnCyrcAdUAzjqjOtlVq9eTRKJxGyfMeNfHQk4S4Cf3nFrPqQwh99/ZzMZhYLo4cOi2+TlEY0fr933at2a6NEj29pJxPbiAgOZDR99ZPvx7Qm+x1XKGTx4MO7cuQOZTeJGnAB8DaAugL0AytlgTPPo1InVa8zKYjOponByAj7/nGWVcHMDDhxgiQpPnLCtrV5eLHcXwDKn/vWXbcd3dPhS0UFQqVRo0aIFjh07pnfcxcUFJ0+etMqY9+7J8M47oXjyRIqaNbPw7bd3oFSq7XKpWMCePUC7dmwz/p9/WPyiIa5eZfnsr19n7RctYqlzTIi1N5lBg1hV7KpV2ZLR1dV2Y9sLfKloAEdfKo4fP76QB7+tPOyvXNE6TjZuTJSebvUhzUKtJmrenNk7bNir26elEfXqpV06DhxIlJlpfTsLePZM+9Bg9GjbjWtPcD8uAziqcP35558kl8v1BEuImMYLF5hnOkDUogXRixc2Hb7EHDrEbJVKiW7ffnX7l0OFoqONO89S7NqlFc6DB203rr1gd8L12WefUf369cnNzY18fX2pe/fudO3aNb02WVlZNGLECPL29iZXV1fq1asXJSUl6bW5e/cuderUiRQKBfn6+tLHH39MeXl5RtvhaML15MkTCgoKKhTzKGQWidOniZRKdnPFxtq/82RsrDbY2lj27iXy8dGGEO3ebT37XmbIEDZupUpEJYgYKxXYnXC1b9+e1qxZQ3FxcXT+/Hnq1KkThYSE0PPnzzVthg0bRsHBwbRv3z46ffo0NW7cmJo0aaL5XKVSUUREBMXGxtK5c+dox44d5OPjQ5MmTTLaDmMvzJEjR+hJUU5ANiQ2NrbQ071u3boJalMBR48yHyiAqHNnopwcoS0yzLFj2owMJQk6uHuXBWcDRCIR0Zw5RPn51rOzgLQ0bZqeESOsP549YXfC9TKPHz8mAHTo0CEiIkpNTSWpVEqbNm3StLl69SoBoGPHjhER0Y4dO0gsFuvNwpYvX05KpZJyjLxzjL0wL+8jiUQikslk5OnpSVWrVqW2bdvSxx9/TPv27SvRjM8YZs+eXWj8ihUrlihg2xYcPMjcDQC2N2Thy2BROnVidv73vyU7LyuL6N13tcu3Hj1sEyq0Z492zL17rT+evWD3whUfH08A6NKlS0REtG/fPgJAz54902sXEhJCX331FRERTZs2jaKiovQ+v3XrFgGgs2fPFjlOdnY2paWlaV4JCQlGXZiXZzrGviQSCbm4uFBAQADVrVuX3nzzTVq0aBElJCS88pocOXKEFAqFXn8KhUIj7vbI7t0sYR/AYu9UKqEtKprTp7UzpytXSn7+ypXa71m9OtHly5a38WWGD2fjhYQIH1dpK+xauPLz86lz587UtGlTzbH169eTTCYr1LZBgwY0fvx4IiIaMmQItWvXTu/zFy9eEADasWNHkWPNmDGjSIF51YXJysqi7du30+jRo6lFixYUGhpKSqXSrOymurM3qVRKHh4eFBwcTGFhYYWcSUUiEc2cObOkl1YQfvuNyMlJ+yTOFsspU+jRg9nYp49p5584QRQcbLtQoYwMotBQNt7QodYdy16wa+EaNmwYVaxYUW8WYi3hMnXGZSy3b9+mRYsWUZ8+fSgqKor8/f1JoVCY5dXesmVLi9hmSzZt0j6JGz5c+LQxRXHhgnb5df68aX08fkzUpo22n7FjrbtEPnBAO5YtHxAIhd0K18iRI6lChQp069YtvePWWiq+jCWeKj579ozWrFlD77zzDjVo0IDKly9Pbm5u5OTkZPZsTOgHAubwww9sKQYQffihfYpXnz7Mvu7dTe/j5VChVq2sGyr0/vtsnAoViFJTrTeOPWB3wqVWq2nkyJEUFBREN27cKPR5web8L7/8ojl27dq1IjfnH+n8laxYsYKUSiVlG5kD19gL06dPH6pevbrFcsnrZkmtUKECNWzYkAYOHEjr1q2j27dv086dO42y395ZvVp7Q9tjds8rV7Qzw1OnzOvrl19YBgqAqHx59vTSGjx/TlSlSsldOhwRuxOu4cOHk4eHBx08eJAePnyoeWXquCYPGzaMQkJCaP/+/XT69GmKiYmhmJgYzecF7hDt2rWj8+fP065du8jX19cq7hAlESXdXPI1a9akLl260CeffFJmC14sWaIVr08/FdqawvTrx2zr2NH8vq5c0WZdlUqJli+3zkzz8GHtbPaPPyzfv71gd8Jl6KbXTUdc4IDq5eVFLi4u1LNnT3r4Umj/nTt3qGPHjqRQKMjHx4fGjh1rFQdUhUKhmSW5u7tTSEgINWnShIYNG1biXPJlkS++0IrXF18IbY0+8fHMpwuwTA6s9HSi3r2133fAAOuECn34Ies/KEg/u2tpwu6Ey15wNM95R+bTT7U385IlQlujT0Ea57ZtLdOfWk30+efWDRV68YK5YgBE/ftbtm97gQuXAbhw2ZbJk7XitXq10NZouXOHLe1g4ZhAa4cKHT2qFcdt2yzbtz3A83Fx7ILZs4EPP2TvhwwB1q8X1p4CKlYE3n2XvZ82jUmrJWjbFjhzBmjQAEhJATp0AObMAdRqy/QfEwOMHcvev/ce8PSpZfp1aKwopHYDn3HZHrVa6wUukTCfL3sgMZFILmd2/fmnZfvOytIGSxe4X1jKlSEri1UGAojeftsyfdoLfMbFsRtEImDJEmDgQJa3/q23gO3bhbYKKF8eKCigNHWq5WZdAKsqtHIly2wqkwHbtrFZWFycZfpet47l///xR+DXX83v05HhwsWxGmIxu4nffhtQqYDXXwf+/FNoq4CJE1lxipMngT/+sHz/777LUjEHBwPx8Syd9E8/md9vgwbAhAns/bBhrPhHWYULF8eqSCRsptCrF5CbC/ToARw6JKxNAQHAqFHs/fTplp11FdCgAdv3atsWyMxk1YXGjmUCbg7TpwMREUy0Cr5DWYQLF8fqODkBGzYAnTuzQhadOwMvpc63OePHs2IZ586xQrHWwNcX2LVLO0v66isgNhZ49Mj0PuVy7ZLx55/ZqyzChYtjE2QyVlknNhZ48YI9eTtzRjh7fHyADz5g72fMsNwTwJdxcgLmzQM2b2ZCeegQqyp0/LjpfUZHA1OmsPcjRpgnhI4KFy6OzXB2BrZuBZo3B9LTWTWeixeFs+ejjwAPD7Z5bu2ZS69ewKlTQI0awP37QIsWwPLlpi9Tp0wBoqKYa8Tw4dZZ7tozXLg4NsXVlW2IN2rEfJ5iY1mZMCHw8tL6R82caf7+06uoUYM9EOjdG8jLY7OlgQPZ8rmkyGRsyejkxJa6Gzda3l57hgsXx+a4u7O9n+hotsncti1w86YwtowZA3h7s9qKP/5o/fHc3YFNm4D589lT13XrgKZNgdu3S95XVBTbrAeAkSOBhw8ta6s9w4WLIwiensw1IiKC3XBt2wJ379reDqWSbdQDwKxZbCZkbUQiYNw4VrzWx4c9IKhfH9i9u+R9TZzI/gN49ox51ZeVJSMXLo5glCsH7N0LhIUB9+4Bbdqw/R9bM2oU4OcH3LoFrF1ru3HbtAHOngUaNmTL5o4dSx4qJJWyWZtUyhx8f/jBevbaE1y4OILi7w/s2wdUrsyEo21b2z8lc3VlMxcA+PRTICfHdmMHBwOHDwNDh7LZ0tSpQM+eQFqa8X1ERLDZIgCMHi2M+NsaLlwcwSlfHti/n93E168Dr71m+0DiYcOAoCAgIQFYvdq2Y8vlwIoVwLffsve//VbyUKFx49g5qalaESzNcOHSYdeuXbgp1C5xGadiRSZegYHApUvMVSI11XbjKxRa36g5c0x70mcugwaxUKGQkJKHCjk5sWWuXA7s2GHbJa8gWDHo224wpyCsVColpVJJFStWpGbNmtHw4cNp8+bNlGXvNegdlCtXiHx9WRaExo1ZplFbkZ2trSb9b60WQXjyhCg2Vptl4sMPiXJzjTt3/nx2jlJJdO+ede20FDyRoAGskXNe91WQf97Hx4fCw8OpR48eNHv2bIqLi7PRNyxdXLjAEvIBRC1asCygtmLVKjaunx8rWCEUKhXRxIla8WrRgkinmHux5zVuzM5p184+qy69DBcuAxh7YfLy8ujQoUM0fvx4io2NpapVq5KnpyfJZDKLFIQtqPgTGBhI/v7+JJPJaNy4cTa6Co7F6dNs1gCw2YetJri5uUSVK7Nx582zzZjF8euvRO7u2rzzxuTLv3aNyNmZnbNypfVtNBcuXAawdCLBJ0+e0OrVq6lfv35Uv359CgoKIldXV5NF7aOPPrKIXaWNv/9m1aMBos6diXJybDPuunXaNMz2kHvy6lVtEkGplGjp0lfPpL76irV3c2Mpq+0ZLlwGsIRwPXz4kJYsWUJvvvkm1a1blwICAsjFxcWs6tW6L5lMRtu3b7fgty4dHDignT307m3dCtIFqFREYWFszFmzrD+eMaSnE73+unbp2L9/8VWFVCqiZs1Y2zZtiPLzbWdrSeHCZQBjL0zPnj2pcuXK5OnpSVKp1CLLw4LN/dDQUGrevDmNGjWKtm7dSllZWbRkyZJCwhcQEODQla2twe7dRDKZNm2xSmX9MTdsYON5eNhPWTC1mmjBAm3hjLp1iV4qDq9HfDyRQsHaLltmOztLChcuA1hjc14ikZBCoSA/Pz+KjIyk119/nRYuXEi3TahP1bt370L9t27d2sRvWzr57TciJyd2Ew4caP0ZRH4+UUQEG2/KFOuOVVL279c+efXyItq1y3DbRYtYO1dXon/+sZ2NJYELlwGMvTAuLi6aWZKnpydVrlyZWrduTR999BHt3LmzREVoS0pGRgZVqlSp0Ixt5syZVhvT0di0STvbGD7c+k/MNm/W7hPZ2yT43j2ihg2ZfSIRq2dZlJjn5xO1bMnatWxpn0tGLlwGcKQqP0ePHiUXFxc9AVMoFHTo0CGhTbMLfvhBW5b+o4+sK15qNVuOAUT2+PA3O5vovfe0+15duxI9e1a43T//aB9yLFpkczNfCRcuAziScBUwd+7cQntsISEhlJGRIbRpgrN6tfZmtfYy7vff2TgKBdHDh9Ydy1S++05bcq1qVaJLlwq3WbZM+z3i421vY3Fw4TKAIwpXAe3atSu0/9WlSxehzRKcJUu04jV7tvXGUauJGjVi44wZY71xzOX0aaKKFZmdLi7s4YIu+flEbduyz5s2tc0DDmMx5f4UEZX2cEwgPT0dHh4eSEtLg1KpFNqcEpOcnIyoqCg8ePBAc0wsFmPs2LHo3r07PD09bWpPWFgYnJycbDpmUXzxBQsuBoAvv2SpmK3Bnj0sdlIuZwkPK1SwzjjmkpzMSsHt2cN+/+ADlrBQKmW/373LMkk8f84KdxRUGxcak+5Pq8moHeHIMy5d/vzzT3J2draI35hxr1kE7CRgGQHjCHidgHr0119X7SaU5JNPtDOvJUusM4ZaTdS8ufahgD2jUhFNmqQfKqS7xF25kh13dmYe9vYAXyoaoLQIFxHRmTNnSCqV2ki49mpugJdf7u5EkZGszPwHHxB9/TVzWbh0iciW23BqNdHkyVq7Vq+2zjgHD2o9103weLE5L4cK/f03O65WsxhG/BvEbg9LRr5UNICjLxUBIDs7G5GRkYiPj9c7Xq5cORyyUoXVCxcUuHlTjvv3Zbh/X4rERPYzOVn6ynN9fYFKlYDQUPZT91WxIiv2YCmIWNGLhQtZWuTvvwf69rVc/wXExrKkh4MGsdxZ9s7166y60JUrbLm4cCEr0JGYyJaM6elsKVmw3BYKU+5PLlwOwFtvvYWNL5VxadasGQ4cOCDIXlNWFnDnDivwcPu2/vvbt1n+8+IQiVjywJcFreAVFMQKnpYEIlYwYvlydu5PP7FqOpbk2DGgSRPW/7VrQNWqlu3fGjx/zoR20yb2e79+wDffsOszaBDbtzt7FqhVSzgbuXAZwFGFa+XKlRgxYgTy8/M1x3x9fXH27FlUsNcdYrC0w7pC9vLrVUn6pFI2KzMkbD4+TPxeRq0G3n0XWLNGW7arSxfLfrfOnVmivv/+l83sHAEithk/YQKQn8+qA23ezNI879jBMqcePcqumRBw4TKAownX9evX0ahRI6TpJB6XSqXYuHEjevXqJaBl5kMEPH5sWNTu3Xt1fUNXV8OiFhLClkMbNrDl6O+/s1TQluLMGVaRRyQCLl8Gata0XN/W5sAB4D//YSXhvLyAr79m4pWayrK+Tp4sjF1cuAxg7IVZu3YtfHx8EBsbC2dnZxtayFCpVIiOjsalS5f0jo8aNQqLFy+2uT1CoFKxYg+GhE3HI8QgXl5MIFNT2Sxi1CigfXvt/pq5/7Q9e7KK3H36GJ9a2V5ITGRL6JMnmfj27An8+iub5Z45A9SubXubuHAZwNgLIxaL8fLlkEgkkMlkcHNzg7+/P6pVq4aYmBj07NkTVS24yTF06FCsWrVK71jdunVx8uRJu/CZsheys9mszJCwGVNkIyjI8IytQoVX769dvMiWWwBw4QIQGWn+97IlOTmsEO6KFex3f39WWaluXeDECa3fl63gwmUAc4TLGEQiEZycnKBQKODp6YmQkBBERESgbdu26NKlS7Gzt40bN6Jfv35Q6ayPvLy8cPLkSYsKY1khPV37sODGDWDxYla5RyxmS8fs7OLPd3Jiy01Dwubnx2Yq//kP8PPPQI8ebC/NFHJzgWXLgH/+AapUYUtcSz5tfRVr1gDDhzMhE4vZHuGsWdrq2LaCC5cBSnJhkpKSsHXrVhw5cgTXrl3Dw4cPkZ6ejuzsbL1NclMQi8UQi8VQKBQQi8V6e1gAm92tXr0aAwYMMGscjpYXL1ih1SNHAG9vtint4lL0bO3u3VdXsnZxYS4ePj7A4cMEQIT585mrRKVKrEK3MYwfzzbMdf+kJBLm/T9/volf1gTOnGFLx4Iq4hIJcPo0UKeO7WzgwmUAS2/Oq1QqHD9+HDt27MCZM2dw584dJCcnIy0tzWRx69u3L34oK2WIbUxGBtugP3GC+ZcdPgzUqFG4XX4+20PTFTNdV4/ERBhRr/AZxOK7kMkewNPzGUJDCU2aBKJPnwaIjFRCoWCitWCB4R7GjbOteD19Crz5JqsqDjBRvnOHPQSxBVy4DGCucKlUKhw4cAC7du3CuXPncPfuXaSkpCAzMxN5eXkmLS91ycrKEuRhQFkiNZWVvD93ju1xHT7MlmclITdXu79240YeRo36FsBQsPKkKQC8jejlIYDbAO4BuA/AB0D2v8fbA2gAicQJmZm2XTbm5zMn3q+/Zr8HB7MN/IAA64/NYxUNYGxIQYsWLcjf358UCoVFcskXZEn19fWl2rVrU8+ePWn+/Pl07do1ysrKogsXLtjoCnCIWDLAgqymISGWKSLRvfsTAohEonME1COgKQG9CBhNwFcEbCHgPAFpBsOn2Oudl/5+RCSRSEgul5O3tzcpFAqSyWS0fPly840uhnHjtDb5+GhDhawJj1U0gKVTNxdkSfXw8KDQ0FBq0aIFjR49mrZv384Lxdo5SUlE1auzG7NKFaLERPP6W768aCESidhr82aW3fbHHzdQv34fULlyAwkYRsB0Ah7+2z6TABej//4WWTkbYMeO2u8hkRAtXmzdhI08VtEAxk5FPTw88OLFC8hkMiiVSgQEBCAsLAzNmjVD165dERoaajujOVbj/n2gRQvg1i0gLAw4dIi5BJSU/Hy2UZ+Y+PInKgAHAOyCTHYOFSrcQ0rKU2RmZiI3Nw9Mfz4DMAlAKoA6AO6WaGw3NzccOnQI0dHRJTf8FSQnsxCgJ0+0x/77X+Y+4eJi8eH4UtEQpSk7BMcy3LlDFBzMZhW1axMlJ5fs/KysLKpSJYoAfwKqEJD17ywl1ohZ02s6szPdQikSAhQE+FFERG3q3bs3ffHFFxT/b8rS8ePHF8qKW716db1ZvkrFSrr9+CP7aWr2h19/1c4cC/L8R0VZp+AGXyoagAsXpyji44kCA9lNGR1ddL52Q+Tl5b0kRgv/FaLfit1aCA5uQGLx03/b3iUgr9Ays7j89nl5eRQTE1NojL59+9LmzUQVKuj3VaECW66awttva/cDfXzYe09Poj/+MK0/Q3DhMgAXLo4hrlzRlvpq3JgVXjUWsVjy7yzJhYDaBPzv3032JQQkaMTjwAHWXjd9sq+vdiaju59kbFGOhIQE8vX1fUnAJASsMbjXVlKSk4kCAlg/w4ZpU1iLREQzZ1quYhAXLgNw4eIUx/nzrD4h/s0Y+uKFceepVGxGU1B1qKgN+uBg7XJtzhxtTvgrV4hycogWLiQaNYr9zMkpue2bNm16KbGkjFjm2hsG7XjVd9Jdam7Zou1j3z6ibt20369TJ22xXHOWqKVauJYsWUIVK1YkuVxODRs2pBMnThh9Lhcuzqs4dYpIqWQ35GuvERn7cHjzZu2spriZzl9/sRkVwKryWJIDB4iA4QQ0I+DKvzasJiBKbylaMPMr7rsUtdRs1Yq9LyjIq/sKCCD68kvzlqilVrg2btxIMpmMvvvuO7p8+TINGTKEPD096dGjR0adz4WLYwx//62tP9i5s/EzoKJu+OBg7Y379Kn2QUDfvpZ3LVi16mVBeUjAGzqzsGEEsNlQcd+hqJmjodnkq14lWaKWWneIRo0aoUGDBliyZAkAQK1WIzg4GO+//z4mTpz4yvMdLR8XRzgOHmSxjdnZLIZv40bjEuzl57N4yIcPgcBAoHlzFvdHxFLHbNsGVKvGYgPd3S1jKxFLSfPeey9nxbgFoB6Yq0UBUgwcOBODB7csVBVKrQbattV1fxCBaZ4ubgAq/nv8DoBMABIAFf79DACeAkjSO8vPD7h3LwxyueGLWCrdIXJyckgikdCWLVv0jvfv35+6detW5DnZ2dmUlpameSUkJPAZF8dodu8mksnYzOHtt80rKLFoEetHJiM6c8ZyNiYmskIlBTOcopZxbKPelAiQrwk4QEC1l46v/rffeNI6zIoJ+FRnzMMEBOid9913ccV+F1NmXGLj5E04kpOTkZ+fD/+XPAT9/f2RlJRU5Dlz586Fh4eH5hUcHGwLUzmlhHbtgF9+YTOtH38Ehg5ls5KScvYs8PHH7P2CBYAlfEXVapYKp2ZNNotzcgKmTGFppEWil1NaDwDwdQlHaARgFIBWYDMsXT4CkACgKoCCTLxqANMAdAeQBqA5gAZ6Z+k6slqKUpmhbtKkSfhIpzpoeno6Fy9OiejalYnWm28C333HsqYuWVJ0rvuiyMhgObtyc4Hu3YH33zffpsuXgSFDWNEOAGjUCFi1Spu1VCZjCQKZJ/9FAM0AZOj10bJlSyxdulTv2MmTrHAGowpY0HgqgP8VYUU+WID4xH9fujwGW0LO/ffFiI4OM/5LGouZM1arY8pS8WX45jzHVL7/XrtBPXascRvrajXbhC/YpH/61DwbsrKIpk1jNR0BIjc3Fj9Y1BL2+fMsCgmpqbdUE4lE9NFHHxnsv8Ctw5RN+Fdt0BvjhlFqnyo2bNiQRo0apfk9Pz+fypcvT3PnzjXqfC5cHHPQfWo3deqr269Zo3UoPXTIvBCcw4eJwsK043ftSnTvXtFt+/XrV2i/qmHDhpSXl/fKcRYvNiw+hp4wvuozaz5VdAjh2rhxI8nlclq7di1duXKFhg4dSp6enpSUlGTU+Vy4OOaie2PPnm243ZUrzMEUIHrrLdP9m549Ixo6VHuevz/Rzz8XPeNbt25doTRM5cqVo9tGltxWq7W+WnJ50W4dxbl8vMod5FWUWuEiIlq8eDGFhISQTCajhg0b0vHjx40+lwsXxxIsWKC9Mb/8kmjy5Mm0fv16ysjIICKizEyiyEj2ee3apvk3qdVEv/yijaEEiIYM0Xqo6xIfH09eXl56guXk5EQbNmwo0fdavZqN4+LC4jcNzRCL847nnvNWgAsXx1J88omuEA1/aWm29N/jSQTUICCMgE4EzCTg0iv3fhIS9ENqqlcnOniwsA15eXkUHR1daFk4ePDgEn+fhw9Z4DRA9MUXJl4UM+HCZQAuXBxLoVYTTZqkK14D/xWOXjrHxhEwioAVBPxN2uynbf5tKyLAiZyd3SgoKIjq129IjRr9Hzk75xD+9cmaOrXosKNRo0YVEqzatWsbtY9VFH36MJvr1SMysQuz4cJlAGMvzPLly/Wm/hxOUajVRCNHFsye1OTl9ZAA1SuesuUQUHjzHKj1r7gVtDtKQDgBILFYrEn/7ezsXOhcDw8Piosr3rmzOLZv1z5EOHvWgheohJgiXKXSj8tURowYAXopAkosFkMqlcLV1RU+Pj6oUqUK6tevj65du6JBgwYGeuKUFvLygPh4IC4OuHSJveLiWC1EACAS4dkzYypKXAdQDUA3ADfBwmNGARgPQAYgHSwj6jdgTp0stA1gxVR0kUgkWLZsGYYOHWry98rIYDUVAVYSrW5dk7sSBIeIVTQXWxSEFYvFcHZ2hoeHB4KCglCzZk20bNkS3bt3h4+Pjznmc2wAEavgUyBQBT+vXWNOpEXh62vIKzwRwBYAf4EJ1iMwYcoBc+BsDmAlgIIaadsAjASr+lM8Li4uePHiRUm+WpGMHs2K5VauzL6nNVIyGwsvT2aAklyY58+f47fffsP+/ftx+fJlJCYmIjU1VVMQ1pzLJRKJIBKJ4OzsDCJCdnY2evfujU2bNpncJ6fkPH2qL05xceyVnl50e1dXICKCeagX/KxaNRvh4cPw/PlanZYEYCGAsQZG9gAwH6ykGcBKkr0PYLNeNXQvLy+EhIQgKioKsbGxaN++PZKSkhAQEGCRMnbHjwNNmjCx/vNPVnNSSLhwGcAa2SEuXryI7du34+TJk7h58yYeP36M1NRUqFSqEvf1+uuvc/GyApmZwJUrhUXq4cOi2zs5sUKxL4tUxYqsRL0uDx8CQUGPAfiBLe/eBOAJ4BqAmi/1LAHQB0zUWMxt48aX8M03HoiKCrHU1zWK3FygXj12Hfr3B9ats+nwRWLK/cn3uIwgOzsbO3bswN69e3Hp0iXcu3cPqampyMrKgkqlMmsWBgC//PILnJycsGTJEgwbNsxCVpcdVCrD+1CG/mlCQ/XFKSKCVfwxpghrfj6resNE6yKA8wCGgS0FNwLYCSAWBbdXaCirDA0A1auz+MIWLWqb/H3NYcECdm18fIAvvxTEBIvAZ1w6NG3aFDdu3MDz58+Rm5ur2Rw1FYlEAmdnZyiVSgQGBqJGjRpo3rw5evTogYCAAPz+++/o3bs3cnU2UZRKJY4ePYrw8HCzxi6NELEAYl1xunQJuHrV8D6Ujw8TJl2RCg83LyfW7NnAtGlsXygry7A46iKVAhMnApMns4BtIbhxA4iMBHJygB9+APr2FcaOl+FLRQMYe2FERob+6z5pLFeuHCpVqoR69eqhU6dOaNy4MZyMyTynwwcffIBFixbpzdzCw8Nx/vz5EvdVWkhJKXofKi2t6PYuLkyYCsSpQKhMqZdYHEeOAK1asfQya9cyAdi7t/hzYmLYLEvI/4vUaqBNG1ZDsn17YOdO4zNdWBsuXAYw9sJ4eXnh+fPnkMvl8PDwQPny5VGrVi20atUKPXr0KJQ50pKoVCrExMTg9OnTescHDhyI7777zmrjCk1mJpsxvSxSDx4U3V4iYUu6l2dRoaGF96EszdOnQJ06bNbXrx/wzTdAlSpAUhITgZfvJJkM+Oor5nZgbdtexerVLCWOiwtLj2NPtY1LZQZUS+BInvO3b98mb2/vQvFn69evF9o0s8jLI7p6lWjTJqLp04l69SKqVq34nOYVKxJ16UI0cSLR+vVEFy4QZWcLY79arQ3HqVaNKCODaO5c9nulSkQ7d2rLnOHfLA4JCcLY+jK6YT1ffim0NYUptTnnzcURc87/+OOPeOedd/SeUnp7e+PMmTMItaf/Ll+CiJW4f3kGdeUK21spinLlit6Hsqd/qq+/Bj74gM2iTpxgTxorVwZSU9nS8eBB1i4ggCUc7NXLfpZiffoAmzaxp4nHjxuXQ9+W8BmXARxpxvUyAwcO1Jt9AaD69eubHJtmSVJSWL6opUtZwdBmzbT/sxf1cnEhatCAaOBAoq++IvrzTzYbsHTVG0tz+rQ2id/ixezYhAmkiSss+H5Dh5asGrYt+O03bVjPuXNCW1M0fMZlAEeccemiUqlQt25dxMXF6R3v06cP+vXrh0qVKll1/OxsEW7dkiM+Xo74eGc8eOCNy5fFuG/A0VsiYY/9X55FVaok/F5PScnIYLnib94EevRgVXVOnwYaN9bmoQ8LA1auBFq0ENTUQqSns5lrYiIwfjzw+edCW1Q0fMZlAEeecekSFxdHSqWyiEBda70WEnCt2ADikBBWg3DCBKIffmBVoYXah7I0ajWr8lPwPR8/ZlV7CmZfIhFLqWyv33fUKGZn5crGV+cWAp4dwgClRbiIiLZu3Voo26X1Xpt1RCqZWMmqRTR9+n36+2+i1FShr4Z1+fZb7TLr//6PqHFjfdFet05oCw1z9Kj2wceePUJbUzw8O0QpJikpCVFRUXj8+LHe8UqVKmH79u1WGfPcOQUyM++gevUc+PioIBL5AmiDsDA/u9vgtTRXrwKjRrH3zZuzKjgqFdvYVqmADh1YyIw9kpvLXB+IgHfeAWJjhbbI8pTyPz/HR6VSoU2bNjhy5Ije8T59+uCnn36y6thl1Xk/K4s9icvKYn5PBU8MdZ8ezp1r4GQ7YP585qvl4wN88YXQ1lgHB9sqLVtMnz4dMplMT7SqVq2KrKwsq4tWWWbECObCATAH2cBAYPNmQKFgx956izmi2iPXrwOffsre/+9/TLxKI1y47JD9+/dDoVDg008/1YQBubq64uTJk4iPj7dIahNOYYiADz9koTwFvPce80Hz8WFhMk5OwCefCGZisajVrOp2bi4L63n7baEtsh58qajDrFmzIBaL0blzZ0Rbol56CUlNTUVkZCQSEhI0x8RiMebNm4dx48bZ3J6yREICMGAAsH8/+71cOWDLFra/RQRMmsSOv/suULWqYGYWy3ffAYcPs+XtN9/YjwOsVbDeswL7wdinFiKRqNCTNZFIRE5OTuTm5kbly5enhg0b0sCBA2ndunX0zILehp06dSo0dseOHS3WP6doVCrm4uDmpl/7ULfsQIETp0JBdP++cLYWh72H9RQHd4cwgDnCZexLLBaTXC4nb29vql69OrVv354mT55MR48eLdbLff78+SQWi/X6Cg4Otqgocorm4kWiRo30XRzc3Ynu3NG2UamIIiLYZxMnCmfrq3jjDeGr9ZgKFy4DlPTCnDlzhmbOnEmdOnWimjVrUrly5cjZ2bmQwJT0VSCMUqmUZDJZoc+dnZ1p3759Vr4anKwsosmTteE6BZWnAaItW/Tbfv89O+7pWXRRVnvAEcJ6ioMLlwGs4YCakZFBGzZsoCFDhlCTJk0oKCioyBJSxgra5MmTLWYbxzAHDrDsDgVC1bEjkZcXez9qlH7bnByi0FD22dy5gpj7StLS2NIWIBo/XmhrTIM7oFqJ69evY8uWLThx4gRu3ryJR48eISMjwyJZUkUiEXJzc8tswkBbkZLC4vW+/Zb9HhjIqtwsXQo8e8bcGxYs0D9n5UqWcjkwkFXFsUemTGGxiJUrAzNmCG2N7eB3iw61a9fG3bt3kZWVZZGKPlKpFC4uLvDx8UFoaCjq1KmDjh07osW/0bjJyckICDCmJh/HVIiAn39mwlMQdDBsGDBvHktVc+AAq+Lz00/6KZWfP2cpmgFtmmZ749gxJrwAsGKFfdpoLbhw6fBy9oWieLmGYkBAAGrWrIkWLVqgV69eJaqhyEXLuty7x5xJ//iD/V6zJptFNWvG3AZmzWLHly9n2Sx0+fpr4NEjluH03Xdta7cxlIWwnuLgwqWDr68v0tPTNVWrC3LJd+nSBQ0aNODLOQchP58l85syBXjxghWqmDKFFauQy4HkZOacqVazeMN+/fTPf/qUhc0AzAtdKrX9d3gVBWE9vr6OXa3HVPidqMPLAcwcx+PiRTYTOXmS/d60KStUUfPfUodEwMCBLEtrWJh2qaXL55+zXFZRUcB//mM7243l5bCecuUENUcQeMgPp1SQlcVKf9Wrx0RLqWRLwMOHtaIFsCXg77+zmddPPwFubvr9JCayTXsA+Owz+0t8qBvW06EDi5ssi/AZF8fhOXCA3cw3b7Lfe/UCFi0CypfXb3f6NHuyCLDqO1FRhfv65BMgO5uF+nTsaF27TeHbb7VhPcuXl/KwnuKwnneG/VCaEglytDx9SjRokNYnKyiosANpAWlpRFWqsHY9exad5/76debECRD99ZdVTTeJBw+IPDyYfV99JbQ1lsOU+9POJsIczqshAjZuZEvAgpKTw4ezLA49ehTd/r33gH/+YdV5vv226JnKtGlsY79LF7Y3Zm+MHs0K4tavb79+ZbaCLxU5DkVRLg6rVhUvNN99x4ROIgE2bAC8vAq3OXOG+XuJRMCcOdax3Rx++w345Rf2HVavZj/LMnzGxXEI8vPZxnqtWky0ZDLmh3XuXPGidfky8P777P2cOUBMTNHtJk9mP/v2BSIjLWu7uaSnM7EGgI8/LnpvrsxhxaWr3cD3uBybCxeIGjbU7mU1a0Z05cqrz3vxgig8nJ3Trh1Rfn7R7fbvZ22kUqJ//rGs7ZZg5EhmX5UqRJmZQltjefgeF6dUkZXFEvjpujh88w1w6JC+i4MhPvyQzbgCAoD/+7+iXRt0kwQOHcpi/uyJY8eAZcvY+xUrtOmjyzp8j4tjl+zfzzbUdV0cFi8GgoKMO//nn1l4j0gEfP894O9fdLtt24ATJ5h7wdSplrHdUuiG9QwYALRtK7RF9gOfcXHsipQUVgqsbVsmWuXLsxTKmzcbL1q3brEbHmCzKUNxfPn5LBQIYLMzewsd/fxzbVhPaa3WYzJWXLraDXyPy/5Rq4k2bCDy89NWiR4xgvlflYScHKL69VkfTZsWnw107VrWzsuLyN4Szl69SiSTMfvWrxfaGuvC83FxHJK7d5kf1s6d7PdatZiLQ5MmJe9r8mTmIe/lBfz4IwwWrs3JAaZPZ+8nTQI8PU0y3SrwsJ5Xw5eKHMHIz2dBwuHhTLQKXBzOnjVNtP74Q5spYc0aICTEcNtvvmE+YUFB2orV9sLq1cCRIzyspzj4jIsjCBcusH2oU6fY782bs830GjVM6+/+fZaXCmB+W927G26bkaF1Mp0xw76e1D18qI2nnD0bCA0V1By7hc+4ODZF18Xh1CnAw4M95j940HTRys9njqNPnwJ16xZOwfwyCxcCT54A1aqxFDf2BA/rMQ4+49Jh8r/u0x06dECTJk144kALs2+fNmYQAHr3Zi4OgYHm9Tt7NvPtcnNjqWrkcsNtk5O1T+hmz7avJIE8rKcEWPFhgd1gbl1FiURCLi4uFBAQQNHR0fT222/TkiVL6OHDhzb6Bo5NcjLRgAFaz/fy5Ym2brVM3wcPEonFrN/vv391+48+Ym2jow170gtBWhq7LgDRhAlCW2NbeHkyAxh7YcypmygSiUgqlZKnpydVrlyZWrduTWPGjKGdO3cWWxC2NKNWs0f5vr5aF4eRI0vu4mCIx49ZKhuACeOruHuXSC5n7XftsowNlqK0h/UUh10J1+3bt2nQoEEUGhpKzs7OVLlyZZo+fTrl5OTotbtw4QI1a9aM5HI5VahQgT7//PNCff38888UFhZGcrmcIiIi6I8//iiRLSW9MPHx8fTFF19Q7969qXbt2uTn50cKhYIkEolZBWF1Z3AFIhkTE1Oi7+Io3L7NahYWzLLCw4n+/tty/avVRJ06sb5r1CB6/vzV5xTk7mrVquh8XEJx9CgTdYBo716hrbE9diVcO3fupAEDBtDu3bvpn3/+oW3btpGfnx+NHTtW0yYtLY38/f2pb9++FBcXRxs2bCCFQkErVqzQtPn7779JIpHQ/Pnz6cqVKzR16lSSSqV06dIlo22xhgNqXl4e7dy5k8aMGUMtW7ak4OBgkwvCxsTElJpZmUrFktwVVIeWyYg++YQ5hlqSL79k/cvlLAj7VVy9ql1SHjtmWVvMISdHGwhuzKyxNGJXwlUU8+fPp0qVKml+X7ZsGXl5eenNwiZMmEBhYWGa3/v06UOdO3fW66dRo0b03nvvGT2uucJ18uRJmjZtGnXs2JGqV69O3t7eJJfLzVpaFrXUHO+opYj/5fx5rdc6QNS8ORMMS3PyJMvkABAtW2bcOb17s/bdu1veHnP45BNml68v2wssi9i9cE2ZMoXq1aun+b1fv37U/aW/pP379xMASklJISKi4OBgWrhwoV6b6dOnU2RkpMFxsrOzKS0tTfNKSEgw6sLUrFmTXF1dSSKRGNyoN/YlFovJ2dmZypUrRzVr1qQuXbrQzJkz6cyZM5rxzpw5Q25ubnrnyeVy+vPPP428ovZBZibbUC5Ie+zhQbRypXU2v1NTiSpVYuP07m3cku/kSe0eW1yc5W0yFd2wnh9/FNoa4bBr4YqPjyelUkkrV67UHHvttddo6NCheu0uX75MAOjKvwmXpFIp/fjSv+rSpUvJz8/P4FgzZswoUkxedWGMnRk5OTmRu7s7hYSEUExMDA0ZMoQ2bNhAGRkZJb0sRES0cOHCQrO3oKAgevLkiUn92ZK9e7W53AGi119nudGtgVpN1KcPG6diRePjC9u2Zee884517DKF/Hw2IwXYXqA97bnZGpsI14QJE155c199aX2QmJhIVapUocGDB+sdt5ZwmTrjCggIIGdnZ/Lx8aHw8HDq0aMHzZ07l+Js9N90t27dCl3L2NhYm4xdUpKTmRAUCFb58kTbtll3zJUr2VhOTsbvU+3Zo00SePu2Vc0rEStWMLtcXYnu3BHaGmGxSZD12LFjMWDAgGLbVNbJxvbgwQO0bt0aTZo0wcqVK/XaBQQE4NGjR3rHCn4vKE9vqE1x5evlcjnkxXkhGuDhw4clPseSbNu2Dc+fP0dERATu3r0LANi7dy/EYjE+/fRTTCnIwSIgRCxv+wcfMO9zkQgYOZKF0CiV1hs3Lk7rST5nDtC4sXG2FiQJHD7cfsJnXg7rqVhRWHscEisKKSUmJlK1atXozTffJJVKVejzgs353NxczbFJkyYV2pzv0qWL3nkxMTE23ZwXgiNHjpBCodCbfbm4uNDRo0cFs+n2baIOHfRdHGxhzosXRLVqsTHbtzd+7+yXX7SzmkePrGtjSSh4UNCgAXsKW9axqz2uxMREqlq1KrVt25YSExPp4cOHmlcBqamp5O/vT/369aO4uDjauHEjubi4FHKHcHJyoi+++IKuXr1KM2bMsAt3CFsxc+bMQg8KKlWqZPJ+mink5TH3A10Xh08/tbyLgyHefZeNGxBgvADl5TH/LoBo+nTr2lcStm5lNkkk7Cksx7T7U0REZI2Z3Nq1azHQQASr7pAXL17EyJEjcerUKfj4+OD999/HhAkT9Npv2rQJU6dOxZ07d1CtWjXMnz8fnTp1MtqW9PR0eHh4IC0tDUprrmesSJs2bXDgwAG9Yy1btsTgwYMRHR1ttXGvXXPG9OlBuHKFpVCoX/8F1q6VIzzcNnGcGzeyfFQiEbBnj/Hpi7/7Dhg8GChXjmVEtYd/9vR0lmvs/n1g4kRg7lyhLbIPTLo/raWi9oQjz7h0efLkCQUGBlrMd6z4l5iAeQTk/bs0TCFgMAEimz2siI8ncndnM5SpU40/LyuLqEIFdt6XX1rPvpIyYgSzqWrVshfWUxy8yk8px8fHB1OmTIHIJpnl1ABqgCUQ+RlATQDfguma9cnNBd58k+XOataM5c0ylmXLgMREoEIFbT1CoTl6lCUFBHi1HkvA87Y4CBcvXkTTpk3x/PlzzTGxWIyoqCh8//33VhkzKckJV6/eQ+vW4QD2aY6HhYVZZTxdJk5k1aW9vYtPwfwy6enAZ5+x9zNnAs7OVjPRaHJytNV6Bg4E2rQR2qJSgBVngHaDIy8Vs7KyKCwsrJAT7Mcffyy0aVZj+3btk8uS+oZNn87OCwsrvlCGLZk1i9nk50f09KnQ1tgfdvVU0Z5wVOHq169fob2nxo0bl5qA7KJISCAqV47d6GPGlOzcR4+Y6wPAXCHsgStXtGE9GzYIbY19wqv8lBL+7//+D4MGDUJ+fr7mmI+PD86dO4cKFSoIaJl1Uam0KZijo1ldwZLw2WfAixcs7XGvXtaxsSToVuvp2BH4z3+Etqj0wIVLh6FDhyIzMxPNmjVDjx49ivXOtwY3b95Ew4YN8ezZM80xqVSKH3/8Ea+//rpNbRGCTz8FDh9mKZg3biw+BfPL3Lmj3fyeO9c+KuOsWgX89Rfg6sqr9VgcK84A7QZzUzeLRCKSyWTk6elJVapUobZt29LHH39M+/bts8iyLS8vj6KjowuNO2TIELP7dhT279cm0zOlAGpB3GTbthY3zSTu3ydSKplNLyU34bwE3+MygLEXxtwMpwW56f39/SkqKor69OlDixYtooSEBINjjho1qlA/kZGRpXof62UePyYKDGQ3+aBBJT8/Lk6bJPDkScvbZwq9evGwHmOxK895e6KknrnZ2dnYvXs39u7diwsXLiAhIQEpKSnIysqCSqWCJS6ZSCQq1I+HhwdOnDhhE3cDe0GtBrp0YQVha9ZkJctcXUvWR8+ewNatbF9r82armFkitm5lNkkkzKUjKkpoi+wb7jlvAGs9Vbx9+zYtXLiQXn/9dapSpQq5uLiYlIBQIpHQ6tWrLWqbo7BgAZuZODsTXbxY8vOPHWPni8XsCZ7QpKZqC3hMnCi0NY4Bf6poBVJTU7F161YcPHgQV65cwYMHD5CWlobs7Gzk5+ebPfuSSCRQqVQWstaxOHFCm3bmf/8Datcu2fm6aWsGDGAzNqGZPBl48ACoWhWYPl1oa0ovXLh0qFixIh49eoS8vDyo1WqT+xGJRJBIJHB2doanpycqVKiA8PBwtGjRAt26dYOnpydUKhVUKhWc7cG1WwBSU1lIj0oFvPEGcxsoKXv2sArYMlnJQoKsBQ/rsR1cuHS4d+/eK9uIxWLIZDK4ubnBz88PlStXRsOGDdG9e3dERkYaPZaTk1OZrZRNxITqzh2W3G/lypK7CqjV2tnWyJFASIilrSwZPKzHtpTNO8cAISEhSE1N1cySIiMj0bZtW3To0AFubm5Cm1dqWLkS2LSJxR/+9BPg6VnyPn75BTh7FnB31wqYkHz+OXDlCuDnB3zxhdDWlH64cOlQkC6ZYz0uXWJpnwHmKNqwYcn7yMsDpk5l7z/+GPD1tZh5JnH1KksnDQBff80CwznWhae14diMFy9Y2Et2NguB+egj0/pZuxaIj2eC9eGHFjWxxOiG9XTqxMN6bAUXLo7NGD2azU4CA4F16wCxCX99WVksXQ0ATJnClopCwsN6hIELF8cm/PgjS6csEgHr15u+vFu6lLkbhIQAw4ZZ1saS8uCBtlrPnDnCPyAoS3Dh4lidmzeB995j76dNA1q3Nq2f1FRtksBZs0oWhG0N3n+fJS5s2BAYNUpYW8oaXLg4ViUnh+37PH8ONG/OhMtUvvgCePaMFZzo189yNprC1q3Ar7+yJ6OrVrHwHo7t4MLFsSoTJjC3hXLlSpaC+WWSkoCFC9n7OXOEFYq0NOY7BgDjxgElcN/jWAguXByr8dtvzD0AYE8CzcmBOGcOkJkJNGoEdO9uEfNMZtIkbViPOTNIjulw4eJYhYQE5kEOMJeFLl1M7+vWLRZCAwifJPDvv7VhPStX8rAeoeDCxbE4KhXw9ttASgpQr575hU9nzGBOp+3amb6xbwkKwnoAYNAgYW0p63Dh4licWbOYb5O7OwvpMefp36VLzH0C0D5RFIp585gfmp8fsGCBsLaUdbhwcSzK/v3a8JeVK4EqVczrb8oUFrj8xhts9iYUV69qhXPRIh7WIzRcuDgW4/FjVqWHCBg8mKWtMYe//wa2b2dPED/91DI2moJazZaIublA585Anz7C2cJhcOHiWAS1Gujfn7kt1KrFZiXmoJskcNAgQMhs1itXMhF1dQWWLeNhPfYAFy6ORfjiC2D3blby/qefABcX8/rbtQs4coTtjwmZSfT+feaLBvCwHnuCCxfHbI4fZ3tRAJtpRUSY159uksD33zfP/8tceFiPfcLzcekwYMAAvHjxAo0bN0bv3r0RGhoqtEl2T2oq8NZbzAWiTx/g3XfN7/Onn4ALFwClEpg40fz+TGXLFvbiYT32By9PpoNYLC6y+IVIJIKTkxMUCgW8vLwQEhKCiIgIxMbGolOnTmU2b3zB077Nm4HKlVloj4eHeX3m5bGiF//8A8yerZ3J2Zq0NGbHw4ds9ie0K0ZpxpTyZFy4dJBKpWZV3NHNR+/v749q1aqhfv366NGjB8LDw03u11755htg+HA2Izl6FGjQwHJ9+vkx8RIqY/bw4cyWqlWBixe5h7w14cJlgJJeGJVKhaNHj+KPP/7A2bNncffuXSQnJ+PFixfIy8uzSEHYAqpXr47r169brD9bcfEi2/fJyWEb82PHmt9nZiYTiocPgcWLhdtT+usvlskCYH5p3EPeuvCCsAawVkHYJ0+e0OrVq+mtt96iatWqkaurK4nF4hIXhK1evTplZWVZ1DZr8vw5UY0arOhpp05E+fmW6XfePNZnaChRTo5l+iwp2dna7zZokDA2lDV4QVgrkJiYiG3btuGvv/7C9evX8fDhQ6SnpyMnJwf5+fkWGePGjRtQKBR48803sWHDBov0aU1GjQKuXQOCgkxPwfwyz56xkBoA+OQTVitRCObNY9+Nh/XYOVYUUrvBWEUvX748SaVSEolEJZ416b5EIhFJpVJSKpUUGhpKLVq0oNGjR9PWrVv1ZlYJCQnk6+urd65EIqHVq1db+5KYzPffa0veHzxouX4nTmT9RkQQqVSW67ckXLlCJJMxOzZuFMaGsogpMy4uXDoYI0oSiYQUCgX5+/tTVFQUvf7667Rw4UK6ffu2yfZt3ryZpFKp3jienp507do1k/u0BjduELm5sRt7xgzL9fvgAZFCwfr97TfL9VsS8vOJmjZlNnTuTKRWC2NHWYQLlwGMvTCVK1cmT09Pqly5MrVu3Zo++ugj+vPPPykvL88mdo4aNaqQUEZGRtps/OLIziaqW5fd2C1bWnZWNHw46zcmRjjBWL6c2eDmRnT3rjA2lFW4cBnAWpvz1iAvL4/q1q1bSMCGDBkiqF2jR7Mbu1w5osREy/UbH0/k5MT6PnTIcv2WhMREIqWS2fD118LYUJYx5f7kIT92hpOTE86ePYv4+Hh4eXlpjq9atQoymQy//PKLzW3atk0bNL1uHVC+vOX6nj6ded137Ai0aGG5fktCQVhPo0baXPIcO8eKQmo3ONKM62XWrVtHEolEb/bl4+NDCQkJNhn/7l0iLy82G/noI8v2fe4c6xdg74Xg11/Z+E5ORBcvCmNDWceU+5M7oDoI/fv3x/fff693rHr16hgyZAg6duxolTFVKmDgwFCcO+eKiIhMfP/9HUilhLCwMDiZWq5Hh86dgR07WN4uIbxAdMN6Jk/WJkDk2BbugGoAR55x6ZKVlUU1a9Y0y1WjZK9P/50RpRJQSXM8Li7O7O9y+LB2phMfb4GLYwLDhjEbqlUjciD/31IH3+Mq5Tg7O1ttdlUYCYCCTaehAG5brGfdJIHvvsvCfGzNX3+xWESAJQoso3HyDgv3nHcQdu3ahe7duyM3N1dzTCaToVWrVvjqq6+sMqZKBRw6dA9t204HoM3mF2ZmOtI//mAZRRUKYeoS6lbrGTwYaNXK9jZwzIMLl52TnJyMyMhIPHz4UHNMIpHg66+/xkgbPAKLirJsf/n52tnW6NEsbMjWzJ3Lwnr8/XlYj6PChcuOadOmDQ4cOKB3rGfPnvj1118Fssh8NmwA4uIAT09tSmRbcuWKfrUeHY8TjgNhkz2unJwc1KlTByKRCOfPn9f77OLFi2jevDmcnZ0RHByM+fPnFzp/06ZNqFGjBpydnVG7dm3s2LHDFmYLxqeffgqxWKwnWpUqVUJGRoZDi1ZurjZ//PjxtheNgmo9eXmssvYbb9h2fI4FseLDAg2jR4+mjh07EgA6p+Owk5aWRv7+/tS3b1+Ki4ujDRs2kEKhoBUrVmja/P333ySRSGj+/Pl05coVmjp1KkmlUrp06ZLR4xv71KJHjx7UokULGjVqVKGAaFtw5MgRUigUek/2XFxc6OjRoza1w1osWcKe4gUEsNQ4tmbZMm1Yz717th+fUzR2GfKzY8cOqlGjBl2+fLmQcC1btoy8vLwoRyf50oQJEygsLEzze58+fahz5856fTZq1Ijee+89g2NmZ2dTWlqa5pWQkGDUhSkuK4REIiFnZ2fy9fWliIgI6tmzJ82fP98igdAZGRlUsWLFQhkmZs+ebXbf9sLz50T+/kw4li2z/fiJiUTu7mz8RYtsPz7HMHYnXElJSVS+fHk6deoU3b59u5Bw9evXj7p37653zv79+wkApaSkEBFRcHAwLVy4UK/N9OnTKTIy0uC4M2bMKFJ8XnVh5HK5WX5PBels3N3dKSQkhJo0aULDhg2jTZs2GZy9devWrVA/sbGxxdrpiMyZw0SjShWi3Fzbj9+jBxu/USPh0uZwisauEgkSEQYMGIBhw4ahfv36uHPnTqE2SUlJqFSpkt4xf39/zWdeXl5ISkrSHNNtk5SUZHDsSZMm4aOPPtL8np6ejuDg4FfanJ2drWfb1q1bceTIEVy7dk2TQDA7O9tgAkEiQl5eHvLy8pCRkYF79+7h6NGj+KbAYegVBAUF4cKFC/Dx8TGqvaPw9Cnw+efs/SefAFKpbcf/9Vdg61Zerac0UWLhmjhxIj4v+Cs0wNWrV/Hnn38iIyMDkwqefdsQuVwOuVxuVh8BAQEYNmwYhg0bZrCNSqXC8ePH8fPPP2PPnj24f/8+MjMzS5wZVS6XY/v27XjttdfMstle+fxzFsQcGcnCe2xJaqo2d/348UDt2rYdn2MdSixcY8eOxYABA4ptU7lyZezfvx/Hjh0rJCD169dH3759sW7dOgQEBODRo0d6nxf8HhAQoPlZVJuCz62JSqXCgQMH8Oeff+LcuXO4c+cOnj59iszMTIsVzZDL5XozvdLG/fus8AXA/Kcskea5JEycyGIRq1UTxtmVYx1KLFy+vr7w9fV9ZbtFixZh9uzZmt8fPHiA9u3b46effkKjRo0AADExMZgyZQry8vIg/Xf9sGfPHoSFhWlSusTExGDfvn344IMPNH3t2bMHMTExJTX9lfj6+uLZs2dm55KXSCSaMmUBAQGoXr06YmJi0KVLF7O9zh2NTz4BsrOBZs1Y6hpbcuQIsGIFe8/DekoZ1tpwe5miNudTU1PJ39+f+vXrR3FxcbRx40ZycXEp5A7h5OREX3zxBV29epVmzJhhNXcIGLn57uHhQZUqVaKWLVvS6NGjafv27XaRpdTeuH6dSCJhm+JHjth2bN1qPYMH23ZsTsmwu6eKuhQlXEREFy5coGbNmpFcLqfy5cvTvHnzCp37888/U/Xq1Ukmk1F4eDj98ccfJRrb2AsTHh5OAQEBFBUVRX369KFFixbZLO9VaaRPHyYcXbrYfuzp09nY/v5E/z6g5tgpPB+XAUpDPi5H4+xZoF49QCQCzp9nG/O24vJloG5d5iH/009Anz62G5tTcky5P3laG45VmDyZ/Xz7bduKlloNDB3Kw3pKO1y4OBbn4EFg927mN/XJJ7Yde8UK4OhRwM0NWLaMzfg4pQ8uXByLopsk8L33gMqVbTf2/fvajBOffQYY4XPMcVC4cHEsym+/AcePAy4uwNSpth171CggI4NV6xkxwrZjc2wLFy6OxcjP1+5tffABYAMfYQ08rKdswYWLYzF++IEl6vPyAsaNs924umE9EybwsJ6yABcujkXIyQFmzGDvJ05kGU5tRUFYT/Xqtl+ecoSBCxfHIqxYAdy9y3LIF8x+bAEP6ymbcOHimE1GBlAQljpjBtuYtwU5OcxnC2Blzlq2tM24HOHhwsUxm//9D3jyhGVgGDjQduN+9pm2Wk8RpQo4pRguXByzSE7Wlvj69FPbJQm8fJmlyQFY2hxeradswYWLYxZz57KlYt26tguv0Q3r6doVeP1124zLsR+4cHFMJiEBWLqUvbdlksBvvtGG9SxdysN6yiJcuDgmM2sW2yBv2RJo1842YyYmMvcHgIklD+spm3Dh4pjEtWvAmjXs/dy5tpn1EGnDeho3BoYPt/6YHPvEalV+HJHOnTvj2bNnqFWrFtq0aYNu3brBzc1NaLPskqlT2V5T9+6AFbJoF8mvvwLbtrGwnpUreVhPWYYnEtRBLBYbLIAhFoshlUrh6uoKX19fVKlSBQ0bNkSnTp3QoEEDa5lul5w6BTRsyGZZFy8CERHWHzM1FahZE0hKAqZM0fqNcRwfUxIJcuHSQaFQmFVxRyQSQSKRQC6Xw8PDA+XLl0etWrXQqlUr9OjRA562jIOxIq+9BuzdC/TvD6xbZ5sx33uPzbKqVwcuXOAe8qUJLlwGMOXCPH/+HL/99hv279+PK1euIDExEc+ePdMUhLXUZXtVcVt7Y98+IDaW+WvduAGEhlp/zMOHtV7xBw9yD/nSBhcuA1gz5/zly5exevVq7N69G4mJiSYVhPXz88OFCxdsUivSHIhYrqtTp4DRo4Gvv7b+mNnZQJ06wPXrLKxn1Srrj8mxLabcn3xzvhiys7Px+++/Y//+/bh06RLu3buH1NRUZGVlQaVSWWzW9fjxYwQGBqJ58+bYv38/nJzs859lyxYmWq6ubJ/JFnz2GRMtHtbD0cU+7xCBKFeuHFJTU6FWq03uQyQSQSwWw9nZGUqlEoGBgahZsyZatGiBbt266c2qnj9/jrp16+LmzZsAgCNHjkAmk2HSpEmYM2eO2d/HkqhUWrH66CPAz8/6Y16+DMybx97zsB6OLnypqIPoFc5Iuk8WfXx8UKlSJURHR6NTp05o3LixyTOlU6dOoXXr1njx4oXmmLOzM/744w+0adPGpD4tzZo1wKBBQLlywD//AB4e1h1PrWbVr48dY2E927ZxD/nSCl8qmklUVBSSk5MRFBRk06eBDRo0wPPnz7FgwQJMnDgRarUa2dnZaNu2LYKDg3Hx4kVBn0hmZ2uTBE6aZH3RAoDly5lo8bAeTpFYsiKtvWJKpVwh6dixIwHQe3Xs2FEwe776ilWFrlCBKDPT+uMlJBC5u7MxFy+2/ngcYTHl/uQhP3bIjh078OzZM1SoUEFzbOfOnZBIJFhQkEPGRqSnAwXbbTNnAgqFdccjAkaO5GE9nOLhwmWneHp6IiEhAfv27YPzv96WarUa48ePh5ubG06dOmUTO776Cnj6FAgLA955x/rjbd7MSpxJpbxaD8cwfI/LzmnTpg2ysrIwZcoUzJ07F0SEFy9eoGHDhvDz88OAAQPQv39/q4z99KkECxZUAyDBe+8l4Pr1dABAWFiYVVw2UlOB999n7ydMsE0oEccx4U8VHQiVSoU2bdrgyJEjNhpxIYAPAJwGoI3HjIuLQ3h4uMVHKwjrCQsDzp/nYT1lBVPuT75UdCCcnJzg7+9vo9FEAAoKFE60+miHDzPRAni1Hs6r4UtFB2HlypUYMWKEXjiRu7s7evbsifHjx1tlTCLg3LnbiI7Wj+0JCwuz6DjZ2dpqPUOGAC1aWLR7TimEC5edc/nyZTRp0gTp6emaYzKZDJs3b0aXLl2sPr4t9pkKwnoCAnhYD8c4+FLRTlGpVIiIiEBERIRGtEQiEcaMGYOcnBybiJYteDmsp5Rk/uFYGT7jskPeffddfPvtt3rH6tevj2PHjtltALYpqNVsaZiXB3TrBvTuLbRFHEeh9NwFFqB58+Z4+vQpKlWqhIYNG6Jr166Ijo622fg//vgj3nnnHahUKs0xb29vnDlzBqG2SHxlYwrCetzdeVgPp2RwdwgdikvdXJDd1NnZWZPdNDw8HC1atDA7nvHOnTuoV68eUlJSNMecnJywbt06vP322yb3a88kJgK1ajEP+SVLmLc8p2zCEwkawNgL4+7ujhcvXpiVZ6uoDBL16tVDly5d0KBBA72lnkqlQkxMDE6fPq3Xx8CBA/Hdd9+ZbIO9QwT06ME85GNigL/+sl1NRo79wYXLAKY6oJ49exbbt2/H6dOn8c8//+Dx48d48eIFcnNzzcrZZYjw8HCcPn1aE+JTWvnlF1b1WioFzp0DrODLynEgeFobCxMdHf3KPa7k5GTMmzcPO3fuREJCgkmpm5VKJY4cOYLIyEhzzHUInj3TD+vhosUxBS5cxXD9+nVs2bIFJ0+eRHx8PB49eoSMjAyLzriUSiXS0tIs0pcjMGECKzEWFma79M+c0gcXLh0sscclEokglUrh4uICHx8fhIaGIjo6Gp07d0aTJk1KlTtDSTl0SFvsgof1cMyh7N5FRVCcaOnmkvfw8EBQUBBq1qyJZs2aoVevXvDx8bGxtY4FD+vhWBIuXDrExMQgOTm52CeBHNOYM4fVYeRhPRxLwO9IHf7++2+hTSiVxMXxsB6OZeHeMxyrkp/PloYqFQ/r4VgOLlwcq7J8OXD8OA/r4VgWLlwcq5GQwMqZAcDcuYBO7Q8OxyysKlx//PEHGjVqBIVCAS8vL/To0UPv83v37qFz585wcXGBn58fxo0bpxdgDAAHDx5EdHQ05HI5qlatirVr11rTZI6FKKjW8/w5C+vh1Xo4lsRqm/ObN2/GkCFD8Nlnn6FNmzZQqVSIi4vTfJ6fn4/OnTsjICAAR48excOHD9G/f39IpVJ89tlnAIDbt2+jc+fOGDZsGNavX499+/bh3XffRWBgINq3b28t0zkW4JdfgO3btdV6eCwix6JYtrQjIy8vj8qXL0+rV6822GbHjh0kFospKSlJc2z58uWkVCopJyeHiIjGjx9P4eHheuf95z//ofbt25fIHkcrCOvopKQQ+fuzgq7TpgltDcfesZuCsGfPnsX9+/chFotRt25dBAYGomPHjnozrmPHjqF27dp6xR/at2+P9PR0XL58WdMmNjZWr+/27dvj2LFjxY6fk5OD9PR0vRfHdowfDzx6xMJ6Jk8W2hpOacQqwnXr1i0AwMyZMzF16lT8/vvv8PLyQqtWrTQ5p5KSkgpVrCn4PSkpqdg26enpyMrKMjj+3Llz4eHhoXkFBwdb7LtxiufQIWD1avZ+1Soe1sOxDiUSrokTJ0IkEhX7unbtmiYAecqUKejduzfq1auHNWvWQCQSYdOmTVb5IrpMmjQJaWlpmldCQoLVx+Toh/UMHQo0by6sPZzSS4k258eOHYsBAwYU26Zy5cp4+PAhAKBWrVqa43K5HJUrV8a9e/cAAAEBATh58qTeuY8ePdJ8VvCz4JhuG6VSCYVCYdAGuVwOuVxu3JfiWIyCsJ7AQODzz4W2hlOaKZFw+fr6wtfX95Xt6tWrB7lcjuvXr6NZs2YAgLy8PNy5cwcVK1YEwOIC58yZg8ePH8PPzw8AsGfPHiiVSo3gxcTEYMeOHXp979mzBzExMSUxm2MDeFgPx6ZY60nBmDFjqHz58rR79266du0aDR48mPz8/CglJYWIiFQqFUVERFC7du3o/PnztGvXLvL19aVJkyZp+rh16xa5uLjQuHHj6OrVq7R06VKSSCS0a9euEtnCnypaF5WKqHFj9hSxe3citVpoiziOhCn3p9WEKzc3l8aOHUt+fn7k7u5OsbGxFBcXp9fmzp071LFjR1IoFOTj40Njx46lvLw8vTYHDhygOnXqkEwmo8qVK9OaNWtKbAsXLuuyeDETLXd3osREoa3hOBqm3J885zzHLBISWLWe589ZLOKIEUJbxHE0TLk/uT8zx2R0w3qaNAGGDRPaIk5Zgefj0qFJkyZITk5GxYoVebplI+BhPRyh4EtFHYorCAsAEokEcrkcSqUSQUFBqFGjBpo0aYLevXtrXDjKCs+eATVrMg/56dOBWbOEtojjqPC6igYw9sJ4eHggIyPD7GIZTk5OcHV1hbe3NypWrIi6deuiQ4cOaN26damZvQ0Zwjzkw8KA8+e5hzzHdLhwGcDUzfmbN29i27ZtOH78OG7cuIGkpCRNebKS1k40hLu7u8PFUh46BLRqxd4fPsw95DnmwYXLANZ8qlhQEHbHjh1ISEhAVlZWiUXNzc0Nf//9t0MUhM3OBqKimIf80KHAihVCW8RxdHglawuiUqlw6tQp/P777zh37hz++ecfJCcn48WLF8jLy7NYQVgAeP78OaKiohAWFobz58/D2Y7XXbNn87AejvBw4dLBzc0NmZmZZu1xicViyGQyuLq6ws/PD1WqVEHDhg3RuXNnREdH67VVqVRo3rw5jh8/DoBVzlYoFOjXrx/+7//+z6zvYg0uXdKKFQ/r4QgJXyrqUNxTRZFIBIlEoklDXaFCBURGRqJly5bo0qUL3NzcTLYvMTERdevWRXJysuaYRCLBd999h/79+5vcryXJzweaNgVOnAC6dwe2bOGFLziWge9xGcDYC9OyZUukpKSgatWqaNSoEbp27Yrw8HCb2blx40b0798feXl5mmOenp44deoUqlatajM7imLxYmD0aFat5+pVoHx5Qc3hlCK4cBnA0UJ+hg4dilWrVukdi46OxokTJwRxp+BhPRxrwkN+SgkrV65EXl6e3lPGs2fPQiqV4v3337epLURMqHhYD8ee4MJlpzg5OeHChQu4du0aPDw8NMeXLFkCmUyGX3/91SZ2bNoE/P47D+vh2Bf8qaKdExYWhtTUVKxcuRIjRoxAfn4+8vLy0Lt3b7i4uOD111/H+PHjrTJ2WpoYI0ZUBSDF4MGPQfQEly8zm0pLBADHMeF7XA7Gf/7zH/z88882Gm0VgHcBXAVQB0AuACAuLs6mDy04pRu+x1XKUalUSExMtNFoYgDu/74fggLR4nDsAT7fdxAmT56MefPm6fmZBQYGYsCAAejbt6/Vxr1+/SbCwvTjesLCwqw2HodjDFy47Jw9e/aga9euyMnJ0Rxzc3PDoUOHCnniWwO+IuTYI3ypaKckJyejfPnyaNeunUa0xGIxFi5ciIyMDJuIFodjr/AZlx3Svn17/Pnnn3rHunXrhm3btglkEYdjX3Dh0iEyMhLJyckIDAxEjRo10Lx5c/To0cNm2U3nzZuHyZMn6+1jhYSE4MKFC/DkEc0cjgbuDqGD6BVRwyKRCFKpFC4uLihXrhxCQ0MRFRVldnbTY8eOITY2FpmZmZpjCoUCu3btQosWLUzqk8NxFHisogGMvTDlypVDWlqa2dlNJRIJZDIZlEolAgICEBYWhmbNmqFr164IDQ3VtCvIw3Xr1i3NMZFIhBkzZmDGjBlm2cDhOApcuAxgqgNqdnY2du/ejf379+P8+fO4d+8eUlJSkJWVBZVKZVberqJo2bIlDh48aNE+ORx7hwuXAazpOX/27Fl8//332L17NxITE5GZmVniGVtAQADOnTtX5ioFcTgAT91sUVJTU7F161YcPHgQV69exf3795GWlobs7Gzk5+dbbLYVEBCAhw8fWqQvDqeswIVLB4VCgezsbJPPL8iS6uzsDE9PT1SoUAHh4eFo06YNunXrZlaWVA6Ho4ULlw663ukvU5BL3s3NDX5+fqhatSoaNmyIrl27OkR1Hg6nNMGFS4fOnTsjNTUVERERaNu2Lbp06WLXFXc4nLIKFy4dtm/fLrQJHA7HCHisIofDcTi4cHE4HIeDCxeHw3E4uHBxOByHgwsXh8NxOLhwcTgch4MLF4fDcTi4cHE4HIeDCxeHw3E4uHBxOByHgwsXh8NxOLhwcTgch4MLF4fDcTi4cHE4HIeDCxeHw3E4uHBxOByHgwsXh8NxOMpEBtSCijzp6ekCW8LhcF6m4L4sSeWsMiFcGRkZAIDg4GCBLeFwOIbIyMiAh4eHUW3LREFYtVqNBw8ewN3dHSKR6JXt09PTERwcjISEBIsXkHVk+HUpGn5disbY60JEyMjIQFBQEMRi43avysSMSywWo0KFCiU+T6lU8j/EIuDXpWj4dSkaY66LsTOtAvjmPIfDcTi4cHE4HIeDC1cRyOVyzJgxA3K5XGhT7Ap+XYqGX5eiseZ1KROb8xwOp3TBZ1wcDsfh4MLF4XAcDi5cHA7H4eDCxeFwHA4uXBwOx+Eo08I1Z84cNGnSBC4uLvD09Cyyzb1799C5c2e4uLjAz88P48aNg0ql0mtz8OBBREdHQy6Xo2rVqli7dq31jbcxS5cuRWhoKJydndGoUSOcPHlSaJOsyuHDh9G1a1cEBQVBJBJh69atep8TEaZPn47AwEAoFArExsYiPj5er01KSgr69u0LpVIJT09PDB48GM+fP7fht7A8c+fORYMGDeDu7g4/Pz/06NED169f12uTnZ2NkSNHoly5cnBzc0Pv3r3x6NEjvTbG3FfFUaaFKzc3F2+88QaGDx9e5Of5+fno3LkzcnNzcfToUaxbtw5r167F9OnTNW1u376Nzp07o3Xr1jh//jw++OADvPvuu9i9e7etvobV+emnn/DRRx9hxowZOHv2LKKiotC+fXs8fvxYaNOsxosXLxAVFYWlS5cW+fn8+fOxaNEifPPNNzhx4gRcXV3Rvn17ZGdna9r07dsXly9fxp49e/D777/j8OHDGDp0qK2+glU4dOgQRo4ciePHj2PPnj3Iy8tDu3bt8OLFC02bDz/8ENu3b8emTZtw6NAhPHjwAL169dJ8bsx99UqIQ2vWrCEPD49Cx3fs2EFisZiSkpI0x5YvX05KpZJycnKIiGj8+PEUHh6ud95//vMfat++vVVttiUNGzakkSNHan7Pz8+noKAgmjt3roBW2Q4AtGXLFs3varWaAgICaMGCBZpjqampJJfLacOGDUREdOXKFQJAp06d0rTZuXMniUQiun//vs1stzaPHz8mAHTo0CEiYtdBKpXSpk2bNG2uXr1KAOjYsWNEZNx99SrK9IzrVRw7dgy1a9eGv7+/5lj79u2Rnp6Oy5cva9rExsbqnde+fXscO3bMprZai9zcXJw5c0bvO4rFYsTGxpaa71hSbt++jaSkJL1r4uHhgUaNGmmuybFjx+Dp6Yn69etr2sTGxkIsFuPEiRM2t9lapKWlAQC8vb0BAGfOnEFeXp7etalRowZCQkL0rs2r7qtXwYWrGJKSkvQuLgDN70lJScW2SU9PR1ZWlm0MtSLJycnIz88v8jsWXIOyRsH3Lu6aJCUlwc/PT+9zJycneHt7l5rrplar8cEHH6Bp06aIiIgAwL63TCYrtGf88rV51X31KkqdcE2cOBEikajY17Vr14Q2k8NxeEaOHIm4uDhs3LjR5mOXunxcY8eOxYABA4ptU7lyZaP6CggIKPT0rODpSEBAgObny09MHj16BKVSCYVCYaTV9ouPjw8kEkmR37HgGpQ1Cr73o0ePEBgYqDn+6NEj1KlTR9Pm5YcXKpUKKSkppeK6jRo1SvPAQTfXXUBAAHJzc5Gamqo369L9ezHmvnoVpW7G5evrixo1ahT7kslkRvUVExODS5cu6f0B7tmzB0qlErVq1dK02bdvn955e/bsQUxMjOW+lIDIZDLUq1dP7zuq1Wrs27ev1HzHklKpUiUEBAToXZP09HScOHFCc01iYmKQmpqKM2fOaNrs378farUajRo1srnNloKIMGrUKGzZsgX79+9HpUqV9D6vV68epFKp3rW5fv067t27p3dtXnVfGWNImeXu3bt07tw5mjVrFrm5udG5c+fo3LlzlJGRQUREKpWKIiIiqF27dnT+/HnatWsX+fr60qRJkzR93Lp1i1xcXGjcuHF09epVWrp0KUkkEtq1a5dQX8vibNy4keRyOa1du5auXLlCQ4cOJU9PT72nQqWNjIwMzd8DAPrqq6/o3LlzdPfuXSIimjdvHnl6etK2bdvo4sWL1L17d6pUqRJlZWVp+ujQoQPVrVuXTpw4QX/99RdVq1aN3nrrLaG+kkUYPnw4eXh40MGDB+nhw4eaV2ZmpqbNsGHDKCQkhPbv30+nT5+mmJgYiomJ0XxuzH31Ksq0cL3zzjsEoNDrwIEDmjZ37tyhjh07kkKhIB8fHxo7dizl5eXp9XPgwAGqU6cOyWQyqly5Mq1Zs8a2X8QGLF68mEJCQkgmk1HDhg3p+PHjQptkVQ4cOFDk38Y777xDRMwlYtq0aeTv709yuZzatm1L169f1+vj6dOn9NZbb5GbmxsplUoaOHCg5j9FR6WoawJA728+KyuLRowYQV5eXuTi4kI9e/akhw8f6vVjzH1VHDwfF4fDcThK3R4Xh8Mp/XDh4nA4DgcXLg6H43Bw4eJwOA4HFy4Oh+NwcOHicDgOBxcuDofjcHDh4nA4DgcXLg6H43Bw4eJwOA4HFy4Oh+Nw/D+38juxQiHx5wAAAABJRU5ErkJggg==",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 300x600 with 1 Axes>"
       ]
@@ -216,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81fd6881",
+   "id": "504d21d5",
    "metadata": {},
    "source": [
     "As you can see, the `full_connect` method inserted one synapse (in blue) from every neuron in the first layer to the output neuron. The `fully_connect` method builds this synapse from the zero-eth compartment and zero-eth branch of the presynaptic neuron onto a random branch of the postsynaptic neuron. If you want more control over the pre- and post-synaptic branches, you can use the `connect` method:"
@@ -224,8 +219,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
-   "id": "c089cb28",
+   "execution_count": 7,
+   "id": "012e7779",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,13 +231,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
-   "id": "cb4497cf",
+   "execution_count": 8,
+   "id": "f2f58f2d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 300x600 with 1 Axes>"
       ]
@@ -258,7 +253,341 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b7a7c5e",
+   "id": "5185b39e",
+   "metadata": {},
+   "source": [
+    "### Inspecting and changing synaptic parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6554cda",
+   "metadata": {},
+   "source": [
+    "You can inspect synaptic parameters via the `.edges` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "319e7096",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>global_edge_index</th>\n",
+       "      <th>global_pre_comp_index</th>\n",
+       "      <th>global_post_comp_index</th>\n",
+       "      <th>type</th>\n",
+       "      <th>type_ind</th>\n",
+       "      <th>pre_locs</th>\n",
+       "      <th>post_locs</th>\n",
+       "      <th>IonotropicSynapse_gS</th>\n",
+       "      <th>IonotropicSynapse_e_syn</th>\n",
+       "      <th>IonotropicSynapse_k_minus</th>\n",
+       "      <th>IonotropicSynapse_s</th>\n",
+       "      <th>controlled_by_param</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>292</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>307</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>56</td>\n",
+       "      <td>299</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>84</td>\n",
+       "      <td>304</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>112</td>\n",
+       "      <td>296</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>140</td>\n",
+       "      <td>282</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>168</td>\n",
+       "      <td>299</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7</td>\n",
+       "      <td>196</td>\n",
+       "      <td>295</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>224</td>\n",
+       "      <td>289</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>252</td>\n",
+       "      <td>299</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10</td>\n",
+       "      <td>23</td>\n",
+       "      <td>280</td>\n",
+       "      <td>IonotropicSynapse</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.0001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    global_edge_index  global_pre_comp_index  global_post_comp_index  \\\n",
+       "0                   0                      0                     292   \n",
+       "1                   1                     28                     307   \n",
+       "2                   2                     56                     299   \n",
+       "3                   3                     84                     304   \n",
+       "4                   4                    112                     296   \n",
+       "5                   5                    140                     282   \n",
+       "6                   6                    168                     299   \n",
+       "7                   7                    196                     295   \n",
+       "8                   8                    224                     289   \n",
+       "9                   9                    252                     299   \n",
+       "10                 10                     23                     280   \n",
+       "\n",
+       "                 type  type_ind  pre_locs  post_locs  IonotropicSynapse_gS  \\\n",
+       "0   IonotropicSynapse         0     0.125      0.125                0.0001   \n",
+       "1   IonotropicSynapse         0     0.125      0.875                0.0001   \n",
+       "2   IonotropicSynapse         0     0.125      0.875                0.0001   \n",
+       "3   IonotropicSynapse         0     0.125      0.125                0.0001   \n",
+       "4   IonotropicSynapse         0     0.125      0.125                0.0001   \n",
+       "5   IonotropicSynapse         0     0.125      0.625                0.0001   \n",
+       "6   IonotropicSynapse         0     0.125      0.875                0.0001   \n",
+       "7   IonotropicSynapse         0     0.125      0.875                0.0001   \n",
+       "8   IonotropicSynapse         0     0.125      0.375                0.0001   \n",
+       "9   IonotropicSynapse         0     0.125      0.875                0.0001   \n",
+       "10  IonotropicSynapse         0     0.875      0.125                0.0001   \n",
+       "\n",
+       "    IonotropicSynapse_e_syn  IonotropicSynapse_k_minus  IonotropicSynapse_s  \\\n",
+       "0                       0.0                      0.025                  0.2   \n",
+       "1                       0.0                      0.025                  0.2   \n",
+       "2                       0.0                      0.025                  0.2   \n",
+       "3                       0.0                      0.025                  0.2   \n",
+       "4                       0.0                      0.025                  0.2   \n",
+       "5                       0.0                      0.025                  0.2   \n",
+       "6                       0.0                      0.025                  0.2   \n",
+       "7                       0.0                      0.025                  0.2   \n",
+       "8                       0.0                      0.025                  0.2   \n",
+       "9                       0.0                      0.025                  0.2   \n",
+       "10                      0.0                      0.025                  0.2   \n",
+       "\n",
+       "    controlled_by_param  \n",
+       "0                     0  \n",
+       "1                     0  \n",
+       "2                     0  \n",
+       "3                     0  \n",
+       "4                     0  \n",
+       "5                     0  \n",
+       "6                     0  \n",
+       "7                     0  \n",
+       "8                     0  \n",
+       "9                     0  \n",
+       "10                    0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net.edges"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "461c7154",
+   "metadata": {},
+   "source": [
+    "To modify a parameter of all synapses you can again use `.set()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8cff6d8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net.set(\"IonotropicSynapse_gS\", 0.0003)  # nS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c55c7d16",
+   "metadata": {},
+   "source": [
+    "To modify individual syanptic parameters, use the `.select()` method. Below, we change the values of the first two synapses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5d381e66",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "net.select(edges=[0, 1]).set(\"IonotropicSynapse_gS\", 0.0004)  # nS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dba8d29a",
+   "metadata": {},
+   "source": [
+    "For more details on how to flexibly set synaptic parameters (e.g., by cell type, or by pre-synaptic cell index,...), see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e522bb2",
    "metadata": {},
    "source": [
     "### Stimulating, recording, and simulating the network"
@@ -266,7 +595,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7b62a1c",
+   "id": "7f6fe380",
    "metadata": {},
    "source": [
     "We will now set up a simulation of the network. This works exactly as it does for single neurons:"
@@ -274,8 +603,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
-   "id": "d27531fc",
+   "execution_count": 12,
+   "id": "a0872a05",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,8 +620,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
-   "id": "c0391a6b",
+   "execution_count": 13,
+   "id": "38359eaa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62a417e2",
+   "id": "5b3c4b39",
    "metadata": {},
    "source": [
     "As a simple example, we insert sodium, potassium, and leak into every compartment of every cell of the network."
@@ -309,8 +638,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
-   "id": "8bdd517a",
+   "execution_count": 14,
+   "id": "bf712140",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,7 +650,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6e331935",
+   "id": "489cb351",
    "metadata": {},
    "source": [
     "We stimulate every neuron in the input layer and record the voltage from the output neuron:"
@@ -329,8 +658,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
-   "id": "93007701",
+   "execution_count": 15,
+   "id": "acf7fbaf",
    "metadata": {},
    "outputs": [
     {
@@ -363,7 +692,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5bfc168",
+   "id": "123ab9c0",
    "metadata": {},
    "source": [
     "Finally, we can again run the network simulation and plot the result:"
@@ -371,8 +700,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
-   "id": "3488d1f0",
+   "execution_count": 16,
+   "id": "7615dd48",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,13 +710,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
-   "id": "cd9871e7",
+   "execution_count": 17,
+   "id": "ebf739d5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Wcgks/BHh0Zs5l56qoRF6igF9RtjPJNJGCjomcx/4BNX45lgexj7LsPqOb/Rr8GJBCnoS4FXdwPQG4rmwKCPVorzoN96HP+AVm7k+5EERuoAWtHT/aSkW5SUUIvZvMZmQKSiaJYQoHnpwfxUxdgBbTmvUSlG2VxCbjDUyKRmU+E/ADCFWpKAnAeobWEQvFwvHlX7Ulzeb+Hro7MstlI6IAjz0IEtEQDdH3ovDWNhilhQBZaMhx2PzJQmI0C0JsjxohaQ5qJeLFHRJBARNMTmXvLEixktk2Aidp+Xi1fDQeb7LUWtwExKhC/TQ2cZoZsFePaDTbVF0hM48DyYTEpKU1CrNlUlRSUSwgmXmvMRaW8TiKztzC/KK2QhdhM8ddC0ETOODetwIimZDZnMCvXpAZbkIPhaFHdh9yVi/h22g5UEHziAPX3rokkhgS+eCl1jHHxG4NUQmXr+VXeIuyh6i01yAY4070RjcOHrPWoMn/8VLgfMVeRyKV+v+MbrKJQGWBy0EkwuLJFGjvoGVh5Rn3xVmeh5vwytRS/9ZQQxqcMXhOrA17iKaigHBSdF0UR46G6EbYbn4b02T4NWvwcck/mP6j5uIpCjTlCzwggtpuUTE888/j5EjRyI9PR3l5eXYunVrok/JUDxBniEjBlwWFvluQp4RVrAw8q/IsfiXBvKMPlkbXlSjKXYGQKtcePdc96hnc4JtkMACG3BtSRH+mPAf03cfBJKixglq4B43G/a5eZBwQV+zZg2WLl2K5cuXY8eOHZg6dSrmzJmDpqamRJ+aYbCtcwEIiXqDKiLivDE1k38cPXQR1yHIquD8ViiKpkcvqAEYQBOVYv3d4LyAMe8UZVenAlCChsRF6P7jy7LFvnnmmWewaNEi3HHHHZg4cSJeeuklZGZm4pVXXkn0qRmGXmTK9+1C/DoAUnFkI10eDaI8KkHnaVvo1TaLaC0gqooGCLbQAL6zGC1YcaXHcnsJnALtB6/qeQi0r03ASlET08tFRujhcTqd2L59OyorK5Xvmc1mVFZWoqqqKoFnZiyBtxX5/s0zCaNE6BZ+dehazf+B+MWLbW4FcJ6p6K7GFZQUFZRAVA/+ov1lrZ4mgOAe7KpBK51jTilStH6XIrta8iKhgt7S0gKPx4OioqKg7xcVFaGhoUHz/zgcDthstqCv/o5e1MXbl+b1MorAwiK+1SjKfgVEnx6PnqDzEwmvxqpKcT3Xff8WvrCIidBTLSZuq43DoR/gJMBDZ5LBQPInRhNuuUTLihUrkJeXp3yVlpYm+pTiRmmdawqOSOLtBsguE2cXSPCK0HlXo4RE6Dzr8XWSiTyFSbM+X5TlEmLPiVr6D+V47PtuRa4WVQc4ohuQacFaTbQOHUj+0sWECnphYSEsFgsaGxuDvt/Y2Iji4mLN/7Ns2TJ0dHQoX7W1tUacqlDY1XhAQMji7QaoLoekL3SO30PXtkbijvwZ3xJgu07yqMf37cNkUq2w5ChMWvX5It+KBPC157TwqhPVBiwu8qiqXBJRB85aTSkWs9L7J9kTowkVdKvVihkzZmDdunXK97xeL9atW4eKigrN/5OWlobc3Nygr/5O4G1Fvn/zigjcKkFXSgw5VrkA/Kb9rN8PgFltGX+tuLoUToRVoReh8/Rd9fxlI5b+A+x1E/eSC/UxE/FOT6UE1RJ8nUX3sYmXlL43EcvSpUtx++23Y+bMmbjkkkvw7LPPoqurC3fccUeiT80w1AKZYjHDajHD6fGix+VBfoz7ZXti+HqX83lphKhqFJF16J6QPAX/aXzQa8tUviubUIzvGFCOAYivDfeoLB5eq43DobaVEpMURdA5ZKRaYO91J73lknBBX7BgAZqbm/HQQw+hoaEB06ZNwwcffBCSKB3IsO8TpaSn+gU9jpuYjdDNzNuF4l3s4tGxXHhF6GYB0SdNiqqtA54iweYA2ERaj8vDTdDZHiOAAUlRptsiIH5GAGjMAK3G14GrraZMA6wmHiRc0AFgyZIlWLJkSaJPI2FQO4AV9AyrBbZed1wPDlvZwfYuj1fE3DrTcH591tV+bfzRoDpCFyFM7HWxmH1vE3K4veh2ulGQZeVyjBDrSPQLLlTBRiApKs5yCVTW+P6dkF4uqs9t1Nua4qXfVbkMRNRiA/ARyeC2vGyEHt/DGBKhc1uwpC0efMo3Ay0QeO+b4tW5LiJeo2eUh64XqYq0HtQzVqXWPgki9GT30KWgJwHq2mKAT4UHO3U1cez7wfa5APgljLyqpCjfTo6+P9WRLc+oT2/mIqKjY0gdugEviQbArfQ17DF18wTGJUXdKqupv3RclIKeBBBVEgjgE/WKaimgvtl5+YshZYsCXkAtcsm8esm6iFr0kDp0wS1t1eIq6tV6wccMHkQC95c4m0f3HEKsJinokj5Q3zwAH0tALWL8ui3SlxT7bh9e4igq2cruW21VcH3Fnc6KXxEtekWsptVCrw5dZMWJ3qDVHedCu1jOQbnHOVWIiUYKehKgntYCzIMaxw2kFkheIqZEbZx9XP2aZ/5liyJe1mDkLEA96Lk8BC4Pf0uCCpuyDN+ASDU0QveJKSHGLf8Ptbb6x1uLpKAnAerKBQBceqKrPV0qYvE2GQpE6HyrUdQ1zzzbpoYkRQW/go49hoie6wFbSuyy9NDfiQGWC9O6FkBQTb/I6hoW9QKuTBmhSyLFGyZCj+fB0SsDjHe/usm/eMsWVUlRvi/L9v2pzifwjGz1InSuto7qGFaLWVlhLEJkQywXA172oM5F0BJQwDgPO6SrpSxblERKIOoKfI/Hg6PuXsirM6LetJ93LxeeguhWRejswh/uL6EWWEmjjhzZhlm9AqpA1HagEe8VVQ9aRh1X6xzUn1smRSV9oo6CAD6WgNpD59UZUf1mIV7VKOqBgk5zubyCTuX7p6WYFV9YxEuoATE12x6dRWiAGLGji9PUeRjjBd13LxglqOoOqKKTz7yQgp4E0JsneOk/hyoXjZYCPF4jplsOyStCNyApKiKyVTcXEyF+WmInUmRDXgtoRB261kI7AS/1DkfIjM7g48eKFPQkIFC2GPgeD8tFSV5aQr35eB5IRVRUC4DiXrCk8vzTmRdQx1uupk6KAvxXi+q9E1WIh65V4ipAZNXle0asFA1ruRgVoatmdIH6e/mCC0kfqH1RgE+ZlFs1XQb4+LpqK4dbfbtHu/FUvH3hAW2rgmdk6/US5WXfvOvzWQKeduB7IlcxqheRGdFGNty6DMOSouo1BQlY3BQLUtCTAM0bmMNDGngRReDXzKOfte6LKOIVdJVvyTNxqS61BPgmLdW953nvn6KVbxFpuQS6VAYPUiIjZfULVADjI/SQlaIGJ2VjRQp6EsC+dJnC4yFVWwAAn5px3eZcnJOiqcybYuJftOT7U3PxFseVqEDodRFhuWiuKhYgdmobjGeiWg+tQSuQFBUfIXu1Bme59F8SKW5VFATw9dBTNT302B+MgIfO11pQJ0UBfrXoamEC+HroNIkGiO3oGK4zp8ikqNoGEyls4QKReHv5R4L6/bMAuLWeFo0U9CQgkOgKfI9HdOfyaEU6PK0czguLBIquVlJURPMvIDS3ILIBGCDWQ9drCcwjUa17TI1By0jLhf1dhnS1lJaLpC8CEUng15GV5ptidnGIpFMsoZF/fFUufnFULT+P9yFXL4QC+ImVZv02x/4cWh66yAZgImYxagghumWLgLi+KpozVgMX9rCCnmrh2yJaNFLQkwAtOyDHL+idvbELOl3SrpUIjEvQafKSczWKum8Iu++4+8RoJUUFeOhmk6/Gnd2/iJdoaF4jztEj2+5HveZAxPECxw2dsWamGrewSGtw5tUHSTRS0JMAWq5nYe7g7HR/hO7g1z4X4PQmJFV9O01YAUCXI54ZhYYtwqkFrWZSVECVC1tRpCQQBfZyAQJNzHgLLBup0nvTYjbBmiK286DWjNXInuhuprePuo0DkNy2ixT0JMCtEXVRy8Xp8cIR46u36H5TLaEPRjyRjmKNMM2T6H474xJ0KPuj5PgHtnj2C2jPgpTBglMdOiBeaOnsSFS1TtCxNPIC7PFEiWtg0Ap8LxGWi9nEvw+SaKSgJwFaycAsJuqN1XZxayRFebTlVZctAgHhtcdhEWldh+w0ToKuWrQEBK5xd5z7BrRrp0X4rl6NBLooD52t3AluH8HHBtND/YrDoGMaIKZujdyT2RxoFdEdx6xZNFLQkwCtMi2L2YSsOKNerbLFTB7lkBpVCFR44xF0raQoj4ECCF20BARsrXj3DTB2EXut/QMGT99V82UogqJXRs81I3RRNeFaEbqR3Q61AhYgcL/EG1yIRAp6EuDRiKSBgO0Sq+C4wlQL8Hy1HQDkpKcCAOy9rpj361YGoMD50v3a4tgvoJ0UVQYLQRE6m0Dk9cZ6LbHJ5lARpYVWbT0gvuOiZr8aA7st0mIC9fOYw2m2KBIp6EmAVsQLsInRWCN0rV4u8a+407JyeHjd9EGyWjQsl3gjdI2kKI9BiKJ1TehLGQB+QuT0VxFZU9hBj99Mg0WrcgcQ36Ar8DwwZbwGJkX7jtDjv19EIQU9CdC7geKNCMJFjfH1Q9eKpOMXFac7NInLS6yUwUKQEHo0qlx49Z9nCZSihs5iuAs6Cf1MgPgEpZblkqU8C4nx0AE+tqJopKAnAVpL3gH2Jo41KRraPpeP8GqIY1r80a7Toz9QxDvNVc6Z2XcuR0HXyoMA/Huf0OOw1z4gNHwjx0BOI/j7oldNaiVF6X0Qr/UWzfFDInRpuUgioa8bKGYPXUNk8jLi96S1xDGbgx/t8u83NUUr+ozvQXZ5QhPE2RwGIYpbx3el4sdLBOi1T9UYpG2CLBfWywbEdJHUPC6jTvQ+cLpjL+ONFD0PXbFcZIQuCYdWRALE76HThz+NqaHN9Qt6R48r5mX6TkH2hZaHzssWCXfOnQ533H1JaALaqpqm0+vNa5oeznLhLXbUWmOvGSC+ha7yu7IE7lsa3ADiLQ/6PKaqfpc50nKRRIJWRALEP8XTskZohO7ykJh7cTg0E3O+/cYTvWhZLrx8y0BkGyroLg+J+wUaTo9P3NTil8vZKqA2iJblAvCNHh0a1wwQ3xs8MFMLLuM1SlD17DNZtiiJiMD7C/kmYWi0xlZbZFktyo3a0RObyAQiadZDj9/H1U5c8rZc2GuRorwoOl7B1Ro8gcAAGuu1VqNlHbFrFniKHZ11hAi64EZVTo37C2CsJU7XUg99C1RM8pknUtCTAFpSx3shg0PDcjGZTErUGKvIBKwc3paLfpVLvLYI3Td7zmazidsMwKGRVwAClouth5PlolFNA4ipdNH6PbPHEpWg1Bp82eOKFlTdOnRZtiiJBK2mVED8UW9fUWMsD6Tb41W68AVN+7nWoYcKupfEFxFqWS4AkMvBKmL3H2q58BU/rcQxwA6o/MRGT1jzOA9SarTKVwExn1ELrbbT7PGl5SIJi1bVCBCcwIwFLcsFYGyA7uj362Q60Wmt6ORRDsnuNyM1YBHF8yBp+fMAR49ewy4CgNwMvjaBVuIY4LvqlaI7SHH+TGq0rDdA3AKq0OOH9ssB+C1yE4kU9CRATwzyM60AgPYYhBdgLRcdGyCGSMfJJA+1kq3xeMVODX/YZDJx8U77FgkxHnogQhdruWSLsFw0fh8A/7yAGq08ASDe6qHQz81alQAz+MsIXRIOPTHIV6a2fC2XeCJ/uk+TKdjzH5Tpty4c7iDRjwbaN5ztPQ0ErsPZGAc2QLt+G+BYFkkHT10Pna/lkqL7OfiJnQjLLhK0rDdAXL29GuV3mapdRiwjdElY9CLpfL9ItsdsuWhHGjRqjEXQqY/tqxAJiEpueiqovrd3O2M5XaUveaZa0P0zlbMx7hcIrNRMT9Xed3tP7PsGwkXo8SWg1VAbTf05eK56peh56PHcP5Ggm+/I4FPx1Bf0GocMKLLKRRIJWnXdAJCf4RObbqcnpgUj3ToRbzxJLdrRTy26ZrOJEd7YHjh6vmqxotF/rAMFu+/QwSL+6B/Qt3Tisbe06PGvHcgIEXS+MwFAv8qF3j/dTo/yuXnipNVOCfLQHS4aoauusT930OPyxDwLFY0U9CRALymakx6ok44lGqIrTLPTtAU9ln3S1YFZzGIWChXetq7ohdfjJcp1YF9p59tvfAMFEFgEoxbCQUqeIr4Ivdd/7qGRM9+KEDrTCBmk45zNaaFfPhj4/YhIjPbloYuP0PXLNeN5Ho1ACnoSEPDsQqNeZXobg5h1KoKeGvT9QUpUGr2IdTm1hdG339jFkV11qN43D8tFb7YSzyDEQgdP9QyACi23CF3n+vMamIKOpWNTpVjMSoJQhLAFqrO0bSVR5ZIUvZmJhX0epaBL9NBbGQcELIFYbiAq6FmqCH1wdhoAoLXTEfU+u3X2CQSEty0GUWH7s6erklEFWf7osyuOCF2xXIKj/3htIgqbW2ChIuR0e7l0XNQTWR6JYzV6NhXAJkb5i2uPznEDpZkGeegpoc9jYHbLb+DkiRT0JCDcDUQf1GhLF10erxJpZKvskYIsn4i1xhCVKhG6NdRyUYQ3BlHpcgQeYpOqu188AwUAEEKUAUMtErwiWyVCTwstdaPVQPHMMCg9OpZLvoAIPZygx7tGIpbj8rav9NArJgCYQgWOAydPpKAnAXpTPADIU6oworuB2MSR2u8e7Bf0WGwGKhh0oGFRvO4Y9kvFju5Da7+xilWvK7C6NUTQs/hEtnoRuslkwiA6gHbGJ7ZsniHEcoljMNWDDoJag3dehhjLhRASZtCK3SqMhsgidCnoEh26HX1PbaMVsxa/nZKXkRqS1BqcHaieibYFKhUM6j2zUOGKJZKmn48KU9B+46xEoQKQwvRuCew7MFjE0ytGr/oHCAyg8QoR+87QkGqdjMDAH28rYErYCF1AVQ3gi46JMvhq22P2XrfSf14EkVhNMkKXaEIIUR5UtdgAgUg42oigxe4T9CE5aSE/y05LUfz61q7ofHQq1vmakXTsN/vZLjpQhO43XjtBif6zrCF2Dj2ey0MUOykW6IwoO12r+if2GRELFU9rilmjnt537T1ewm0lIw00ssIIG+9Ile3XE5IcZ2aFPKt51ASqwzSeRwHVRDyRgp5gguyAMDdQtA9Osz9CL8wOFUiTyaRE6dHaAG3+7akPzxKPcJ0NN1Awtkgs0ScdLAo09p1htShWVyxWUeAY+pZRQTYvQfcJDY2OWdJTLYoAxpM8ZgnMOrQsFzEROrV50lLMIc3qUixmJcnMM1egpjOcoPtnQqJb+MaKFPQEwzacytQoBYx1itfiF97C7NAIHQgIcrQiU9fRAwAYlpce8rNBcVgLYa0cv0h6vCSmqoq2MHYOu/9Yp9FeL1EiNq2BriCO3AILLX2k/rWaeMpRtVB+J1o2WBx5mHAog5ZGjib4uOIElb6IWkvQY7VAjUIKeoKh07ssqyXkJdEAuzQ9uhv49NluAEBxbqjwArFXutS1+wR9+KCMkJ8NjiP5V9/RCwAYqmERsdFnLKJIHz4tsQUCs6BYq2jsvW6l5Wp+mNxCLFVFLPRz5OmIXawJdD3OKsfTGKQECTptwaCVdAf4rEnoi07/wKm1eE7EAi6eSEFPMNR7zdGYRgNs2WJ0N/Dx5i4AwJih2Zo/L4yhFr3X5VEi/+H5oYI+1D94dDrcUb8HtbbNNwCVDc7SPt8c34PcHEPt/Bn/IDQ0R3twi7eKpt7m239+ZqpmqRuvpGijzffZi3QGaR4tElg6wsya4il9DQedFWgNjOy5iIyQA88kv5yWUUhBTzAt/qTkYA2vm/1+tFHvseZOAMDoQm2BjCXCOtLo22d+ZqpmlJidlqJUBjTZoxPeGiroBZmaP6di3GSLXtBrWn37HjFYe9/0GjdHec6U023+WYvGIAeAW9kincXoCzofawfwLe6hydXBWaGzJpqbiTap3hdn+5iFFHBaCKYHIUR5JrSeSTpDiGXlthFIQU8wtBplsI7XTatUmu2OiBOCTfZenD7bA5MJmDAsV3MbRcSiiHgP1HcAACaV5IZUi1Co2DTZeiPeb0e3Cw3+7UfqiG5RLr0Oke+Xcqo1/GBBzzlWQaczgPM0bCiAX4TeECZ/AQTsAB5iR3Ml2WkpSlMqlgK/yLfFOUipaexj0BJtudh63MpLorUsOsVDP9ci9JEjR8JkMgV9PfbYY0Hb7NmzB7Nnz0Z6ejpKS0vxxBNPiDqdpIVOWQt1/F1qjTg93ogTglXHWgEA44tydCMdKgoNHZEL5Ocn2gAAk4fn625DB6DGKMRxz5l2AL4IWqvKBWAi9ChF1+H24GiTb2YxVsd+or59tPumHPfPhvQGjED1T3wiUHvWJ7LFOoJO75WWGGwpNWf8xyrJT9ccvKnYdTk9XFoaKMdt7/UfV2e2Qy0XQUlRGuBkp6WEXSnawbHenyfa6XJO/OY3v8GiRYuUf+fk5Ch/t9lsuOaaa1BZWYmXXnoJe/fuxfe+9z3k5+fjrrvuEnlaSUUgQtcWsvRUC3LSU2DvdaPZ7tAVaJZ3dtcDACovKNLdpiTP98DQJGdfON1efFLdDAC4Ylyh7naKOEYRoW867NvvRWWDdLcZEqPoHqizwenxoiDLqm/n+KP/xijOmWXvGTpzydP8eQEToXu9RDP53RdeL0F1gx0AML44R3Mbeu0bY7Cl1NT6k+p6NlJuegpSLSa4PAStXU7d7aJFSbrr7C8/jsVrkUA/t95siz5/tN5fq4Q0kQgV9JycHBQXF2v+7NVXX4XT6cQrr7wCq9WKSZMmYdeuXXjmmWfOKUGvt9HqDu2oC/CJGRV0vSiTcqy5E+sPNQIArp9WorsdjYDqOnojEpl/7atHa5cTQ3PScPGoAt3t6OeI1L5we7x4f28DAGDOJP0BKNYoev2hJgDAJSML9G2iGKN/wFeltK/OBgCYcp62oA/OtsJk8olAW7dTt5Q0HKfautHpcCPVYsKYIeFnGrHYUmr2+z/T+GJty85kMqEgy4pGmwNtnfwE/USLL5mvJ6iik6J95VvSUy3ItFrQ7fSgrdOZdIIu1EN/7LHHMHjwYEyfPh1PPvkk3O6AZVBVVYUrrrgCVmsgMp0zZw6qq6tx9uxZ3X06HA7YbLagr/4MvYHKdG4gILqp9FP/roaX+KLzcUXakRzgm7abTL7Iu69KhV6XB09/eBgAcOusESGtBFhK8n3ieDrCyP+NHWdwpr0HgzJTceX5Q3W3GxqDN9/r8mDNF7UAgHmTtQML376jn1VQPj7YCKfbi7KCTIzSSUCnWsyK2EY6I1JDZzHTywbpXn8lfxGjdcSys6YdADB5uPYgBQR8dF6J0SZ7LxpsvTCZgAt0cj8FnFbd6kEHlBE61VZA7LNFIxAm6D/60Y+wevVqbNiwAT/4wQ/w6KOP4qc//any84aGBhQVBUdk9N8NDQ26+12xYgXy8vKUr9LSUjEfwCBOtdIbSF/Q2cRoON7fW49/7WuAxWzC0q+cH3bbVItZiUz7EplH3jmAmrZuFOWm4c7LR4XdltoadKAKx5n2HvzXewcAAHdfOSakGRMLjQBPn+2J2Lt8bt0RNNkdKMlLx7wLh+luN8R/HWy97qj8YIfbgz+sPwoAuPGi4bozAAAYplhc0Q8aXi/Bav/A9JUwNtrQ3MB94vXG7u/WtHbjYL0NZhNQPlp/NhbPugMtPj/uy9GMG5qtWQMOsAO7GDHdfsoXTE4q0R5QAHYm1M8F/ec//3lIolP9dejQIQDA0qVLcdVVV2HKlCm4++678fTTT2PlypVwOOK7CMuWLUNHR4fyVVtbG9f+Ekl7t1NJdOr5u0DAEqjv0Bfelk4HfvXWPgDAvVeNwcQwNySFRtPUN9Ti7V1n8NrWGphMwFPfnKr7oFFoZHOytSus8Np7Xbhz1Rew97oxrTS/z4GitCADJpOvxj2S2uc1X9TghY3HAAAPzZ+o2TmPkpueoqwKPH02sgja4yX4xRv7cKSpEwVZVnz30pFht6cDUrQROiEET39UjYP1NmRZLfjGjPN0ty3MToPJBLj91k4sEELw7Me+2dhlYwvD2kOKZ8/B4gGA17f5nuVwuR+aELY73NzfXNTQ0Yv9db58yCUR2IpNnD43T6Ly0O+//35897vfDbvN6NGjNb9fXl4Ot9uNkydPYvz48SguLkZjY2PQNvTfer47AKSlpSEtLXoPMhk54q++KM5N1+yXQaHR+ymdqJcQgl+/tQ9tXU5MKM7BD788LqLjjxmSjR017Tjsry9Xc7DehmVv7AUA/PBLYzF73JA+90kHJnuvG+3dLqUGm8Xp9uKev+3AoQY7CrPTsHLhdKSEsXEAX2/qkrwMnGnvwanWrrBC88/ddfi5/7zvumI05oaJzgGfHzxicCb219lwqrWrzzyF2+PFA2t3461ddbCYTXjqm1N0q3MotKoo3KCsxuXx4ldv7sMav9D94qsXaF5PSqrFjMFZVrR0OtFo643aq+91efCrt/bhjZ1nYDYBP6kMP8ujq4XPRDgI6uH2ePHcuiP49EgLUswmLLhYf9adnZaiFAk02np1F+RFCyEET/z7ELzEl2+hMyotktlyiUrQhwwZgiFD+n6otdi1axfMZjOGDvX5pBUVFfjlL38Jl8uF1FTfL+Wjjz7C+PHjMWiQfrXDQGKLv7xwsk4yjUL9dbr4Rs27e3xWS4rZhKe+OTVsNMpCqyWqG0LzEC2dDnz/z9vQ7fTg8rGF+HEfDzclw2pBSV466jp6Ud1ox6zRg4N+TgjBz/+xB5uPtiDTasGqOy5GaZjZCUtZQSbOtPfgZEs3ZozQjqA+PtCIpWt2gRDg2+VlWDZvQkT7Hjk4C/vrbIqHqofT7cVP1uzE+3t91/u5hdPx5Qn6ESVFSUJHaLl0OdxY/Pcd2FjdDLMJ+M31F+KW8hERHael04nath7dqhstatu6cc+r27HvjM9qefi6SZgxIvxzSD/TmRjzAoCv5PP+tbsVz/7+a8aH9a8B3+Bo7+1EfUcvxg7VzxNFysmWLjz8zn5srG6GyQTcf034ez2Qc0k+QRfioVdVVeHZZ5/F7t27cfz4cbz66qu47777cOuttypi/e1vfxtWqxV33nkn9u/fjzVr1uD3v/89li5dKuKUko6ObhdW/eckAOCaieEFYaT/Bj/V2h1iYzTZe/Hrt31Wy5Ivj8WFYZJYaib4KxgO1AcLusPtwT1/244z7T0YOTgTf/j29JDOd+GgA9Se0+0hP3vqw2q8sfMMLGYTXrjloqjO9/wiX+RMKzDUfHa0Bff+fQfcXoKvTyvB/7v+wrC+NsvIQt+gcrJVX9Adbg/ufXU73t/bAKvFjJdunYFrJ4eP/il00Drex4AB+H6nC16uwsbqZqSnmvHybTNx66y+xRyAkpjta2Bi2XS4GfP/sBn7zthQkGXFX+8sx3cqRvb5/2K1kQBfXuDP/zmJa5/7FDtr2pGTloKnvjkVd1+pPcNnodFzfRRrKLTodLjx1L+rcc3vNmFjdTNSLSY8ftMUlKuCEDUDxnKJlLS0NKxevRoPP/wwHA4HRo0ahfvuuy9IrPPy8vDhhx9i8eLFmDFjBgoLC/HQQw+dEyWLHi/Bj9fsRGuXE2OGZOHr04eH3X54fgZSLSb0uDw4fbZHEQdftLsX7d0uTByWi8VfGhvVeUwtzYPFbEJtWw9q27pRWpAJQgh++eY+fHHyLHLSU/A/t1/cp52gZsp5+fj3/kbsqm0P+v6qz07g+Q0+X3vFDZNx1Xj9qha9/QKnNAeKbSfbsOgv2+B0e3HNxCI89c2pUdV7j/NHenqDRbfTjbv+sh2bj7YgLcWMP942I6rzn+CfDR1r6oTL49WtVDnW3InbX9mK02d7UJBlxf/ePhPTw9Tnq6GD/8kIBN3rJXjxk2N46sNqEAJMPS8PL9w6I+ISRGq50ER1pINnXXsPHvy/3fjsqG+GetnYwXjiG1MjPq5iX8WQYAZ8Fs/r207jmY8OK5Vjs8cV4uHrJumWhLLQhnexViyJRIigX3TRRdiyZUuf202ZMgWffvqpiFNIap7+sFqJvn5/8/SwZYCA74UGE4flYvfpDuysbVcE/S9Vp7D+UBOsKWY8s2Bqn/tRk5Oeihllg7D1ZBv+vb8Bd14+Co++fxD/t/00zCZg5cLpffrJWlSM8UU4n1Q3o9flQXqqBa9trcHD7/gqWu6rPB/fCuOT6jG1NB+AbyFPj9OjVMVsPdGG7/5pK7qdHsweV4iV3+7bk1czvcy37/1nbHC4PUGrBDu6Xbhj1VbsqGlHptWC//nOTFw6Vn9xlRbD8zOQZbWgy+nByZYuzZLSbSfb8P2/bEN7twsjBmfiz3dcgpE6pZB6jB7i25728tHD1uvC/a/vxkcHfHmrmy8uxcPXTQp5cUY4SgdlItViQrfTgzPtPThvUHjrjBCCt3fV4ddv7YPd4UZ6qhnL5l2A22aNiGrwpfd/uNmUHp8cbsaj7x1EdaNvkdbIwZn4+bwLMGdSUdSzuZq2bni8JKrZq2iELiyShPLennql+uLxm6ZEbDlMLxuE3ac78MWJNlw3tQT7znTgt+8fBAD88toLFPskWq6bVoKtJ9vw4sZj2HK8FR8f9C3EWXFj9BG0cq6l+Rie70tg/vGT4/B4vXjOX963aPYo/Ojq6GYSlDFDslBakIHath6sO9SIr00pwadHmvGDv25XvP6Xb5upuWS7L8oKMlGQZUVblxM7TrUrg1Kz3YHb/vdzHGqwIy8jFavuuDiqiJliNptwwbBcbDt1FjtqzoYI+r/21uPHa3bB6fZiamk+Xrl9pm5/n3DQ+u39dTa4PV7Nge1wox13/3U7jrd0wWox4zfXT8LNl5RFfSxrihljhmTjUIMdh+rtYQW9o9uFX729D+/srgPgG0Cf/uZUjI4gIlYzzh9kHPaLciScaOnC8n/uV+r58zJS8aOrx+G2WSMizjlRSvIyYE0xw+n24szZnrBrSIxGNucykIP1NjywdjcAn7BdPy281cJy5fm+ZPS7e+pwpNGO7//ZZy98ecJQfKciMn9Vi2/OPA+jh2ShtcuJjw82wWI24bc3XIgFF0f/gFNMJpPihf7u48OKmN91xWj84toLIo6EtPZ73VTf6tenPzyM3753ALe/EojM/+f2mWFr2fva99UTfAPY27vOAPAJxk0v/kepxlnzg1kxiTmFRvWbDrco3yOE4OVNx3Dv33fA6fai8oKhWL1oVkxiDgBjh2QjJz0FPS4PDjWECt67e+rw9ec/w/GWLpTkpWPt3RUxiTmFWkkH6/UX+P3nWAvm/n4T3tntqwq6/yvnY+0PKmIScyCQzD/S1Kn0odfD6fbiD+uPYM6zm7DpsM8nv/PyUfjkwatw5+WjohZzwDc4j/JbW8daws+EjEZG6AbR3u3EXX/dhh6XL5L82dzIqi8os8cVKtUjX/ndJgC+ZlO/WzAtZoEEfOWAry2ahZc+OQaH24ubLy71e9XxcUv5CBxv6cJrW2tQnJuO+75yflQDmB6LZo/Gmi9qcaKlC//96QkAvkU9j94wOSq7QItvzDgPa7efxtrtp+HyELy/tx49Lg9KCzLw1++VR21/qPnS+CF4bt0RrDvUiNZOB9JTLXjo7f34x47TAIBbZ5Xh4fmToraLWMxmE2aOGIQN1c1Yf6hJmQG6PV48/sEh5ZpdOmYwVi6cHvPAQZlWmo+3dtWh6ngrfnh1cLmsw+1bYfzfnx4HIb6E7e8WTMM0v3UWK6WDMhX76lCDTbeaZ9vJNvzizb1KWe7scYX4zfUX6q7ojYZxRdmobrRj/5kOfCnGmawIpKAbgMvjxZK/70RtWw9KCzIiqrtWk2Ix4/FvTMFdf9mOHpcH08vy8dKtMyJq1tUXRbnpWD5/Utz7YTGbTVg+fxL3/eZnWrH6rllYuf4ouhwe3HTRcMy9sDiuQY1SPnow5k8twTu76xSRvXxsIZ5bOF33bUfRMK00H1POy8Oe0x24+eUt6HS4Ud/RC7MJeOhrE3H7pSO5fI6vTSnBhupmvL6tFnddMRr1Hb24b80uJUn9gytH48Frxsc1cFCuHD8UeOcAvjjZho5ul9LC92C9DUtf361E7gsvKcOvvnpBnwvTIsFsNuGSUQXYUN2M/xxtDRH0jh4XnvjgEF79vAaAb0Xrr782EddPK+FyfQFgxohBeHdPvbKyVAtCCP65uw7D8jLCLlTiiYkkYw/IKLDZbMjLy0NHRwdyc2PzkUXi9RIse2Mv1myrRUaqBf+459KIVnHq0dHtQnNnL8YMyeZ2c0oCuDxevLa1BkebOnHpmMKokmWRsOd0O25+eYvydvvh+Rl48htTok6yhqPb6cZVT25Ek92BYXnpaLY74PYS5KSn4ImbpmBehKWWkUAIwbzff4pDDXYs/tIY3HvVWLyy+QSeW38ELg9BQZYVj980BV/pozQ3Wv538wn817sHfDOExZcp5/Le3no88s4BZVn+t2aeh2Xzwi/IioU9p9tx3R8+Q05aCr74VWXI7HDP6Xb85p0D2HbqLCYU5+DdH14e1QAaq65JQRcAIQRHmjrx0YFGvLO7Doca7DCbgJdvm4lKzje2pP9R29aNDw80ojDbimsmFsfs+4dj85EW3PXXbcrAcdX4IfjtDZO5dUVkeXdPHZb8fScAwGI2Kb525QVFWHHjZGVlJU+a7Q5c9th6OD1ePPmNKThvUCb+sOGIUgo5ujALv71hspLc5o3XS3DZ4+tR39GLlQunY74/t9Nk68WT/67G/+04DUKAjFQL7rlqDH5w5eiokvVS0BMs6C6PF1tPtOHjg41Yd7ApaFWnNcWMx26cjBsv0u/DIZHwptnuwK7adowqzOSyolIPQgie/vAwXth4FF7iKwW87yvn47qp/CwOLR59/yBe3nQ86HvWFDPuvWoM7r5yTNw5lb545sNqPLf+KIbnZ2DZtROw7eRZrP6iBr0uLwDghunD8dO548O2EdBDCnoCBN3p9mLz0Wa8s7seHx9sVF4uC/hurEvHDMacScWYM6mYiwcrkSQzZ7uc6HS4cd6gDEPsQKfbi0ffP4i122phTTFj7oXFuOfKsYaVEXY63Jj77KaQhm7Ty/Lxq69O7LN1QjikoBsk6B4vwefHW/HP3XX4176GoLd/F2Zb8eUJQ3H1BUW4fGwhlwSQRCJJXuo7evDkv6txuNGOUYXZ+OaM8zB7XGHcA1qsuiYVJ0Jq27qxdlstXt92WnmhMeDrvPbVycMwf+owTC8dFNPrxSQSSf9kWF4GnvnWtESfhoIU9DA43B58dKARa76oxeajLaBzmfzMVMy70Cfi5aMGJ9XSX4lEcu4iBV2Dlk4HXt1Sg79uORX02rfZ4wqx4OJSfGViUUzLyyUSiUQkUtAZjjd34o+fHMebu87A6fZlqoty0/CtmaX41szSiPt2SyQSSSKQgg6fP/77dUfwxo7ToK0hpp6Xh+9dPgrXTh4WdRdDiUQiSQTntKC7PF68vOk4nlt3BA5/RH71hKG456oxmDFikFyJKZFI+hXnrKA32x24+2/blV4MFaMH46dzx8fVTU8ikUgSyTkp6G6PF3f9dZvv1VfpKXjkukm4YfpwGZFLJJJ+zTkp6FXHW7Gr1ifmb957WUxv5ZFIJJJk45wU9NnjhuDv35+Fjh6XFHOJRDJgOCcFHYCwLmwSiUSSKGQ9nkQikQwQpKBLJBLJAEEKukQikQwQ+r2HTrv/2mz6bx2XSCSS/gTVs2i7m/d7Qbfb7QCA0tLSBJ+JRCKR8MVutyMvL6/vDf30+xdceL1e1NXVIScnJ6qFQTabDaWlpaitrU2ad5EmA/K66COvjTbyuugT67UhhMBut6OkpARmc+TOeL+P0M1mM847L/Z3debm5sqbUAN5XfSR10YbeV30ieXaRBOZU2RSVCKRSAYIUtAlEolkgHDOCnpaWhqWL1+OtLS0RJ9KUiGviz7y2mgjr4s+Rl+bfp8UlUgkEomPczZCl0gkkoGGFHSJRCIZIEhBl0gkkgGCFHSJRCIZIJyTgv78889j5MiRSE9PR3l5ObZu3ZroUxLKww8/DJPJFPQ1YcIE5ee9vb1YvHgxBg8ejOzsbNx0001obGwM2kdNTQ2++tWvIjMzE0OHDsWDDz4It9tt9EeJm02bNmH+/PkoKSmByWTCW2+9FfRzQggeeughDBs2DBkZGaisrMSRI0eCtmlra8Mtt9yC3Nxc5Ofn484770RnZ2fQNnv27MHs2bORnp6O0tJSPPHEE6I/Wlz0dV2++93vhtxDc+fODdpmIF6XFStW4OKLL0ZOTg6GDh2Kr3/966iurg7ahtfzs3HjRlx00UVIS0vD2LFjsWrVquhPmJxjrF69mlitVvLKK6+Q/fv3k0WLFpH8/HzS2NiY6FMTxvLly8mkSZNIfX298tXc3Kz8/O677yalpaVk3bp1ZNu2bWTWrFnk0ksvVX7udrvJhRdeSCorK8nOnTvJ+++/TwoLC8myZcsS8XHi4v333ye//OUvyRtvvEEAkDfffDPo54899hjJy8sjb731Ftm9eze57rrryKhRo0hPT4+yzdy5c8nUqVPJli1byKeffkrGjh1LFi5cqPy8o6ODFBUVkVtuuYXs27ePvPbaayQjI4P88Y9/NOpjRk1f1+X2228nc+fODbqH2tragrYZiNdlzpw55E9/+hPZt28f2bVrF7n22mtJWVkZ6ezsVLbh8fwcP36cZGZmkqVLl5IDBw6QlStXEovFQj744IOozvecE/RLLrmELF68WPm3x+MhJSUlZMWKFQk8K7EsX76cTJ06VfNn7e3tJDU1laxdu1b53sGDBwkAUlVVRQjxPexms5k0NDQo27z44oskNzeXOBwOoecuErVweb1eUlxcTJ588knle+3t7SQtLY289tprhBBCDhw4QACQL774QtnmX//6FzGZTOTMmTOEEEJeeOEFMmjQoKBr87Of/YyMHz9e8Cfig56gX3/99br/51y4LoQQ0tTURACQTz75hBDC7/n56U9/SiZNmhR0rAULFpA5c+ZEdX7nlOXidDqxfft2VFZWKt8zm82orKxEVVVVAs9MPEeOHEFJSQlGjx6NW265BTU1NQCA7du3w+VyBV2TCRMmoKysTLkmVVVVmDx5MoqKipRt5syZA5vNhv379xv7QQRy4sQJNDQ0BF2LvLw8lJeXB12L/Px8zJw5U9mmsrISZrMZn3/+ubLNFVdcAavVqmwzZ84cVFdX4+zZswZ9Gv5s3LgRQ4cOxfjx43HPPfegtbVV+dm5cl06OjoAAAUFBQD4PT9VVVVB+6DbRKtL55Sgt7S0wOPxBF1YACgqKkJDQ0OCzko85eXlWLVqFT744AO8+OKLOHHiBGbPng273Y6GhgZYrVbk5+cH/R/2mjQ0NGheM/qzgQL9LOHuj4aGBgwdOjTo5ykpKSgoKBjQ12vu3Ln4y1/+gnXr1uHxxx/HJ598gnnz5sHj8QA4N66L1+vFT37yE1x22WW48MILAYDb86O3jc1mQ09PT8Tn2O+7LUr6Zt68ecrfp0yZgvLycowYMQKvv/46MjIyEnhmkv7CzTffrPx98uTJmDJlCsaMGYONGzfi6quvTuCZGcfixYuxb98+bN68OdGnoss5FaEXFhbCYrGEZKAbGxtRXFycoLMynvz8fJx//vk4evQoiouL4XQ60d7eHrQNe02Ki4s1rxn92UCBfpZw90dxcTGampqCfu52u9HW1nZOXa/Ro0ejsLAQR48eBTDwr8uSJUvw7rvvYsOGDUHtunk9P3rb5ObmRhV0nVOCbrVaMWPGDKxbt075ntfrxbp161BRUZHAMzOWzs5OHDt2DMOGDcOMGTOQmpoadE2qq6tRU1OjXJOKigrs3bs36IH96KOPkJubi4kTJxp+/qIYNWoUiouLg66FzWbD559/HnQt2tvbsX37dmWb9evXw+v1ory8XNlm06ZNcLlcyjYfffQRxo8fj0GDBhn0acRy+vRptLa2YtiwYQAG7nUhhGDJkiV48803sX79eowaNSro57yen4qKiqB90G2i1qVYMr39mdWrV5O0tDSyatUqcuDAAXLXXXeR/Pz8oAz0QOP+++8nGzduJCdOnCCfffYZqaysJIWFhaSpqYkQ4iu7KisrI+vXryfbtm0jFRUVpKKiQvn/tOzqmmuuIbt27SIffPABGTJkSL8sW7Tb7WTnzp1k586dBAB55plnyM6dO8mpU6cIIb6yxfz8fPL222+TPXv2kOuvv16zbHH69Onk888/J5s3bybjxo0LKs9rb28nRUVF5LbbbiP79u0jq1evJpmZmUldnhfuutjtdvLAAw+QqqoqcuLECfLxxx+Tiy66iIwbN4709vYq+xiI1+Wee+4heXl5ZOPGjUElm93d3co2PJ4fWrb44IMPkoMHD5Lnn39eli1GysqVK0lZWRmxWq3kkksuIVu2bEn0KQllwYIFZNiwYcRqtZLhw4eTBQsWkKNHjyo/7+npIffeey8ZNGgQyczMJDfccAOpr68P2sfJkyfJvHnzSEZGBiksLCT3338/cblcRn+UuNmwYQMBEPJ1++23E0J8pYu//vWvSVFREUlLSyNXX301qa6uDtpHa2srWbhwIcnOzia5ubnkjjvuIHa7PWib3bt3k8svv5ykpaWR4cOHk8cee8yojxgT4a5Ld3c3ueaaa8iQIUNIamoqGTFiBFm0aFFIEDQQr4vWNQFA/vSnPynb8Hp+NmzYQKZNm0asVisZPXp00DEiRbbPlUgkkgHCOeWhSyQSyUBGCrpEIpEMEKSgSyQSyQBBCrpEIpEMEKSgSyQSyQBBCrpEIpEMEKSgSyQSyQBBCrpEIpEMEKSgSyQSyQBBCrpEIpEMEKSgSyQSyQBBCrpEIpEMEP4/PAAw6W4GMr4AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -403,10 +732,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1931cacf",
+   "id": "708c519c",
    "metadata": {},
    "source": [
-    "That's it! You now know how to simulate networks of morphologically detailed neurons. Next, you should learn how to modify parameters of your simulation in [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/03_setting_parameters.html)."
+    "That's it! You now know how to simulate networks of morphologically detailed neurons. We recommend that you now have a look at how you can [speed up your simulation](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). To learn more about handling synaptic parameters, we recommend to check out [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)."
    ]
   }
  ],
diff --git a/docs/tutorials/03_setting_parameters.ipynb b/docs/tutorials/03_setting_parameters.ipynb
deleted file mode 100644
index b7051c3f..00000000
--- a/docs/tutorials/03_setting_parameters.ipynb
+++ /dev/null
@@ -1,1005 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "57786f67",
-   "metadata": {},
-   "source": [
-    "# Setting parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4eedc5ad",
-   "metadata": {},
-   "source": [
-    "In this tutorial, you will learn how to:\n",
-    "\n",
-    "- set parameters of `Jaxley` models such as compartment radius or channel conductances  \n",
-    "- set initial states  \n",
-    "- set synaptic parameters  \n",
-    "\n",
-    "Here is a code snippet which you will learn to understand in this tutorial:\n",
-    "```python\n",
-    "cell = ...  # See tutorial on Basics of Jaxley.\n",
-    "cell.insert(Na())\n",
-    "\n",
-    "cell.set(\"radius\", 1.0)  # Set compartment radius.\n",
-    "cell.branch(0).set(\"Na_gNa\", 0.1)  # Set sodium maximal conductance.\n",
-    "cell.set(\"v\", -65.0)  # Set initial voltage.\n",
-    "\n",
-    "net = ...  # See tutorial on Networks of Jaxley.\n",
-    "fully_connect(net.cell(0), net.cell(1), IonotropicSynapse())\n",
-    "net.IonotropicSynapse().set(\"IonotropicSynapse_gS\", 0.01)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05042d73",
-   "metadata": {},
-   "source": [
-    "In the previous two tutorials, you learned how to build single cells or networks and how to simulate them. In this tutorial, you will learn how to change parameters of such simulations.\n",
-    "\n",
-    "Let's get started!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9bf6ff26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import jax.numpy as jnp\n",
-    "from jax import jit, vmap\n",
-    "\n",
-    "import jaxley as jx\n",
-    "from jaxley.channels import Na, K, Leak"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6a84822",
-   "metadata": {},
-   "source": [
-    "### Preface: Building the cell or network\n",
-    "\n",
-    "We first build a cell (or network) in the same way as we showed in the previous tutorials:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "fb0314f0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dt = 0.025\n",
-    "t_max = 10.0\n",
-    "\n",
-    "comp = jx.Compartment()\n",
-    "branch = jx.Branch(comp, nseg=2)\n",
-    "cell = jx.Cell(branch, parents=[-1, 0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "707d56f6",
-   "metadata": {},
-   "source": [
-    "### Setting parameters in `Jaxley`\n",
-    "\n",
-    "To modify parameters of the simulation, you can use the `.set()` method. For example\n",
-    "```python\n",
-    "cell.set(\"radius\", 0.1)\n",
-    "```\n",
-    "will modify the radius of every compartment in the cell to 0.1 micrometer. You can also modify the parameters only of some branches:\n",
-    "```python\n",
-    "cell.branch(0).set(\"radius\", 1.0)\n",
-    "```\n",
-    "or even of compartments:\n",
-    "```python\n",
-    "cell.branch(0).comp(0).set(\"radius\", 10.0)\n",
-    "```\n",
-    "\n",
-    "You can always inspect the current parameters by inspecting `cell.nodes`, which is a pandas Dataframe that contains all information about the cell. You can use `.set()` to set morphological parameters, channel parameters, synaptic parameters, and initial states. Note that `Jaxley` uses the same units as the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbecd214",
-   "metadata": {},
-   "source": [
-    "### Setting morphological parameters\n",
-    "\n",
-    "`Jaxley` allows to set the following morphological parameters:\n",
-    "\n",
-    "- `radius`: the radius of the (zylindrical) compartment (in micrometer)  \n",
-    "- `length`: the length of the zylindrical compartment (in micrometer)  \n",
-    "- `axial_resistivity`: the resistivity of current flow between compartments (in ohm centimeter)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7dcda4bb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell.branch(0).set(\"axial_resistivity\", 1000.0)\n",
-    "cell.set(\"length\", 1.0)  # This will set every compartment in the cell to have length 1.0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "6a197c8b",
-   "metadata": {},
-   "outputs": [
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-       "      <td>1.0</td>\n",
-       "      <td>-70.0</td>\n",
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-      "text/plain": [
-       "   global_cell_index  global_branch_index  global_comp_index  \\\n",
-       "0                  0                    0                  0   \n",
-       "1                  0                    0                  1   \n",
-       "2                  0                    1                  2   \n",
-       "3                  0                    1                  3   \n",
-       "\n",
-       "   local_cell_index  local_branch_index  local_comp_index  length  radius  \\\n",
-       "0                 0                   0                 0     1.0     1.0   \n",
-       "1                 0                   0                 1     1.0     1.0   \n",
-       "2                 0                   1                 0     1.0     1.0   \n",
-       "3                 0                   1                 1     1.0     1.0   \n",
-       "\n",
-       "   axial_resistivity  capacitance     v  controlled_by_param  \n",
-       "0             1000.0          1.0 -70.0                    0  \n",
-       "1             1000.0          1.0 -70.0                    0  \n",
-       "2             5000.0          1.0 -70.0                    0  \n",
-       "3             5000.0          1.0 -70.0                    0  "
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cell.nodes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b176efd5",
-   "metadata": {},
-   "source": [
-    "### Setting channel parameters\n",
-    "\n",
-    "You can also modify channel parameters (again, units are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)). Every parameter that should be modifiable has to be defined in `self.channel_params` of the channel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5025c06c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cell.insert(Na())\n",
-    "cell.branch(1).comp(0).set(\"Na_gNa\", 0.1)  # S/cm^2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8f547029",
-   "metadata": {},
-   "outputs": [
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>global_cell_index</th>\n",
-       "      <th>global_branch_index</th>\n",
-       "      <th>global_comp_index</th>\n",
-       "      <th>local_cell_index</th>\n",
-       "      <th>local_branch_index</th>\n",
-       "      <th>local_comp_index</th>\n",
-       "      <th>length</th>\n",
-       "      <th>radius</th>\n",
-       "      <th>axial_resistivity</th>\n",
-       "      <th>capacitance</th>\n",
-       "      <th>v</th>\n",
-       "      <th>controlled_by_param</th>\n",
-       "      <th>Na</th>\n",
-       "      <th>Na_gNa</th>\n",
-       "      <th>eNa</th>\n",
-       "      <th>vt</th>\n",
-       "      <th>Na_m</th>\n",
-       "      <th>Na_h</th>\n",
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-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-70.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   global_cell_index  global_branch_index  global_comp_index  \\\n",
-       "0                  0                    0                  0   \n",
-       "1                  0                    0                  1   \n",
-       "2                  0                    1                  2   \n",
-       "3                  0                    1                  3   \n",
-       "\n",
-       "   local_cell_index  local_branch_index  local_comp_index  length  radius  \\\n",
-       "0                 0                   0                 0     1.0     1.0   \n",
-       "1                 0                   0                 1     1.0     1.0   \n",
-       "2                 0                   1                 0     1.0     1.0   \n",
-       "3                 0                   1                 1     1.0     1.0   \n",
-       "\n",
-       "   axial_resistivity  capacitance     v  controlled_by_param    Na  Na_gNa  \\\n",
-       "0             1000.0          1.0 -70.0                    0  True    0.05   \n",
-       "1             1000.0          1.0 -70.0                    0  True    0.05   \n",
-       "2             5000.0          1.0 -70.0                    0  True    0.10   \n",
-       "3             5000.0          1.0 -70.0                    0  True    0.05   \n",
-       "\n",
-       "    eNa    vt  Na_m  Na_h  \n",
-       "0  50.0 -60.0   0.2   0.2  \n",
-       "1  50.0 -60.0   0.2   0.2  \n",
-       "2  50.0 -60.0   0.2   0.2  \n",
-       "3  50.0 -60.0   0.2   0.2  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cell.nodes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f07ece2",
-   "metadata": {},
-   "source": [
-    "For more flexible indexing into the `cell`, see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/00_jaxley_api.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcbfc12b",
-   "metadata": {},
-   "source": [
-    "### Setting synaptic parameters\n",
-    "\n",
-    "In order to set parameters of synapses, you have to use `net.SynapseName.set()`, e.g.:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "081c6d5d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from jaxley.synapses import IonotropicSynapse\n",
-    "from jaxley.connect import fully_connect\n",
-    "\n",
-    "num_cells = 2\n",
-    "net = jx.Network([cell for _ in range(num_cells)])\n",
-    "fully_connect(net.cell(0), net.cell(1), IonotropicSynapse())\n",
-    "\n",
-    "# Unlike for channels, you have to index into the synapse with `net.SynapseName`\n",
-    "net.IonotropicSynapse.set(\"IonotropicSynapse_gS\", 0.1)  # nS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17552e82",
-   "metadata": {},
-   "source": [
-    "You can inspect synaptic parameters and states with `net.edges`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e4ce6aa1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>global_edge_index</th>\n",
-       "      <th>global_pre_comp_index</th>\n",
-       "      <th>global_pre_branch_index</th>\n",
-       "      <th>global_pre_cell_index</th>\n",
-       "      <th>global_post_comp_index</th>\n",
-       "      <th>global_post_branch_index</th>\n",
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-       "      <th>type</th>\n",
-       "      <th>type_ind</th>\n",
-       "      <th>pre_locs</th>\n",
-       "      <th>post_locs</th>\n",
-       "      <th>IonotropicSynapse_gS</th>\n",
-       "      <th>IonotropicSynapse_e_syn</th>\n",
-       "      <th>IonotropicSynapse_k_minus</th>\n",
-       "      <th>IonotropicSynapse_s</th>\n",
-       "      <th>controlled_by_param</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
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-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>IonotropicSynapse</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.75</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   global_edge_index  global_pre_comp_index  global_pre_branch_index  \\\n",
-       "0                  0                      0                        0   \n",
-       "\n",
-       "   global_pre_cell_index  global_post_comp_index  global_post_branch_index  \\\n",
-       "0                      0                       7                         3   \n",
-       "\n",
-       "   global_post_cell_index  local_edge_index               type  type_ind  \\\n",
-       "0                       1                 0  IonotropicSynapse         0   \n",
-       "\n",
-       "   pre_locs  post_locs  IonotropicSynapse_gS  IonotropicSynapse_e_syn  \\\n",
-       "0      0.25       0.75                   0.1                      0.0   \n",
-       "\n",
-       "   IonotropicSynapse_k_minus  IonotropicSynapse_s  controlled_by_param  \n",
-       "0                      0.025                  0.2                    0  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.edges"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e7541044",
-   "metadata": {},
-   "source": [
-    "If you want to set individual synaptic parameters, you can use the `.select()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "8a8c3cd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.select(edges=[0]).set(\"IonotropicSynapse_gS\", 0.1)  # nS"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4b4f3db",
-   "metadata": {},
-   "source": [
-    "For more details on how to flexibly set synaptic parameters (e.g., by cell type, or by pre-synaptic cell index,...), see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c9e29e96",
-   "metadata": {},
-   "source": [
-    "### Setting initial states"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03d3aaed",
-   "metadata": {},
-   "source": [
-    "Finally, you can also set initial states. These include the initial voltage `v` and the states of all channels and synapses (which must be defined in `self.channel_states` of the channel. For example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "daae2c07",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "net.set(\"v\", -72.0)  # mV\n",
-    "net.IonotropicSynapse.set(\"IonotropicSynapse_s\", 0.1)  # nS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "6817c927",
-   "metadata": {},
-   "outputs": [
-    {
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>comp_index</th>\n",
-       "      <th>branch_index</th>\n",
-       "      <th>cell_index</th>\n",
-       "      <th>length</th>\n",
-       "      <th>radius</th>\n",
-       "      <th>axial_resistivity</th>\n",
-       "      <th>capacitance</th>\n",
-       "      <th>v</th>\n",
-       "      <th>Na</th>\n",
-       "      <th>Na_gNa</th>\n",
-       "      <th>eNa</th>\n",
-       "      <th>vt</th>\n",
-       "      <th>Na_m</th>\n",
-       "      <th>Na_h</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5000.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>-72.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.05</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>-60.0</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   comp_index  branch_index  cell_index  length  radius  axial_resistivity  \\\n",
-       "0           0             0           0     1.0     1.0             1000.0   \n",
-       "1           1             0           0     1.0     1.0             1000.0   \n",
-       "2           2             1           0     1.0     1.0             5000.0   \n",
-       "3           3             1           0     1.0     1.0             5000.0   \n",
-       "4           4             2           1     1.0     1.0             1000.0   \n",
-       "5           5             2           1     1.0     1.0             1000.0   \n",
-       "6           6             3           1     1.0     1.0             5000.0   \n",
-       "7           7             3           1     1.0     1.0             5000.0   \n",
-       "\n",
-       "   capacitance     v    Na  Na_gNa   eNa    vt  Na_m  Na_h  \n",
-       "0          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  \n",
-       "1          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  \n",
-       "2          1.0 -72.0  True    0.10  50.0 -60.0   0.2   0.2  \n",
-       "3          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  \n",
-       "4          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  \n",
-       "5          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  \n",
-       "6          1.0 -72.0  True    0.10  50.0 -60.0   0.2   0.2  \n",
-       "7          1.0 -72.0  True    0.05  50.0 -60.0   0.2   0.2  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.nodes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "41d41028",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pre_locs</th>\n",
-       "      <th>post_locs</th>\n",
-       "      <th>pre_branch_index</th>\n",
-       "      <th>post_branch_index</th>\n",
-       "      <th>pre_cell_index</th>\n",
-       "      <th>post_cell_index</th>\n",
-       "      <th>type</th>\n",
-       "      <th>type_ind</th>\n",
-       "      <th>global_pre_comp_index</th>\n",
-       "      <th>global_post_comp_index</th>\n",
-       "      <th>global_pre_branch_index</th>\n",
-       "      <th>global_post_branch_index</th>\n",
-       "      <th>IonotropicSynapse_gS</th>\n",
-       "      <th>IonotropicSynapse_e_syn</th>\n",
-       "      <th>IonotropicSynapse_k_minus</th>\n",
-       "      <th>IonotropicSynapse_s</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0.25</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>IonotropicSynapse</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.025</td>\n",
-       "      <td>0.1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   pre_locs  post_locs  pre_branch_index  post_branch_index  pre_cell_index  \\\n",
-       "0      0.25       0.25                 0                  1               0   \n",
-       "\n",
-       "   post_cell_index               type  type_ind  global_pre_comp_index  \\\n",
-       "0                1  IonotropicSynapse         0                      0   \n",
-       "\n",
-       "   global_post_comp_index  global_pre_branch_index  global_post_branch_index  \\\n",
-       "0                       6                        0                         3   \n",
-       "\n",
-       "   IonotropicSynapse_gS  IonotropicSynapse_e_syn  IonotropicSynapse_k_minus  \\\n",
-       "0                   0.1                      0.0                      0.025   \n",
-       "\n",
-       "   IonotropicSynapse_s  \n",
-       "0                  0.1  "
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "net.edges"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b5efaa4",
-   "metadata": {},
-   "source": [
-    "### Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "60e49bb3",
-   "metadata": {},
-   "source": [
-    "You can now modify parameters of your `Jaxley` simulation. In [the next tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html), you will learn how to make parameter sweeps (or stimulus sweeps) fast with jit-compilation and GPU parallelization."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/tutorials/04_jit_and_vmap.ipynb b/docs/tutorials/04_jit_and_vmap.ipynb
index 6ab50e4b..f8d681f8 100644
--- a/docs/tutorials/04_jit_and_vmap.ipynb
+++ b/docs/tutorials/04_jit_and_vmap.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d69f9c19",
+   "id": "a1074115",
    "metadata": {},
    "source": [
     "# Speeding up simulations"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06431ca8",
+   "id": "d3e38494",
    "metadata": {},
    "source": [
     "In this tutorial, you will learn how to:\n",
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6f3e0ae",
+   "id": "ea191f70",
    "metadata": {},
    "source": [
     "In the previous tutorials, you learned how to build single cells or networks and how to change their parameters. In this tutorial, you will learn how to speed up such simulations by many orders of magnitude. This can be achieved in to ways:\n",
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5925570",
+   "id": "ca8ac1e5",
    "metadata": {},
    "source": [
     "### Using GPU or CPU"
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "989509fd",
+   "id": "694e9c23",
    "metadata": {},
    "source": [
     "In `Jaxley` you can set whether you want to use `gpu` or `cpu` with the following lines at the beginning of your script:"
@@ -76,8 +76,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "e6310cec",
+   "execution_count": 1,
+   "id": "f23c8747",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee69ea31",
+   "id": "49d24dba",
    "metadata": {},
    "source": [
     "`JAX` (and `Jaxley`) also allow to choose between `float32` and `float64`. Especially on GPUs, `float32` will be faster, but we have experienced stability issues when simulating morphologically detailed neurons with `float32`."
@@ -95,8 +95,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "57819f8b",
+   "execution_count": 2,
+   "id": "6082571d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "562b943c",
+   "id": "ee8102e8",
    "metadata": {},
    "source": [
     "Next, we will import relevant libraries:"
@@ -113,8 +113,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "d0a2f5cc",
+   "execution_count": 3,
+   "id": "6cf78785",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc507aee",
+   "id": "03812c92",
    "metadata": {},
    "source": [
     "### Building the cell or network\n",
@@ -139,8 +139,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "d1b3f821",
+   "execution_count": 4,
+   "id": "581ccd8d",
    "metadata": {},
    "outputs": [
     {
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e623d65a",
+   "id": "540877f4",
    "metadata": {},
    "source": [
     "### Parameter sweeps\n",
@@ -184,8 +184,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "72ee5a77",
+   "execution_count": 5,
+   "id": "c916acb9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c675044e",
+   "id": "3b5d7365",
    "metadata": {},
    "source": [
     "The `.data_set()` method takes three arguments: \n",
@@ -210,7 +210,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84771469",
+   "id": "47ac2b5e",
    "metadata": {},
    "source": [
     "Having done this, the simplest (but least efficient) way to perform the parameter sweep is to run a for-loop over many parameter sets:"
@@ -218,8 +218,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "9c6df5a1",
+   "execution_count": 6,
+   "id": "3884b17d",
    "metadata": {},
    "outputs": [
     {
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32d197de",
+   "id": "258e2d34",
    "metadata": {},
    "source": [
     "The resulting voltages have shape `(num_simulations, num_recordings, num_timesteps)`."
@@ -248,7 +248,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11fa1aa9",
+   "id": "d6fd93ba",
    "metadata": {},
    "source": [
     "### Stimulus sweeps\n",
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d5b9a09",
+   "id": "6c82d4f1",
    "metadata": {},
    "source": [
     "### Speeding up for loops via `jit` compilation\n",
@@ -276,8 +276,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "b9718e8d",
+   "execution_count": 7,
+   "id": "b293fe5b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -286,8 +286,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "id": "8c148e70",
+   "execution_count": 8,
+   "id": "9c3cb96d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,8 +297,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "0fc0ea2c",
+   "execution_count": 9,
+   "id": "c13179b2",
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6610cbc4",
+   "id": "7ecf907e",
    "metadata": {},
    "source": [
     "`jit` compilation can be up to 10k times faster, especially for small simulations with few compartments. For very large models, the gain obtained with `jit` will be much smaller (`jit` may even provide no speed up at all)."
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9dd4329",
+   "id": "65e38925",
    "metadata": {},
    "source": [
     "### Speeding up with GPU parallelization via `vmap`\n",
@@ -335,8 +335,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "id": "7a51a292",
+   "execution_count": 10,
+   "id": "364322dd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b5f379f",
+   "id": "489a1f8d",
    "metadata": {},
    "source": [
     "We can then run this method on __all__ parameter sets (`all_params.shape == (100, 2)`), and `Jaxley` will automatically parallelize across them. Of course, you will only get a speed-up if you have a GPU available and you specified `gpu` as device in the beginning of this tutorial."
@@ -354,8 +354,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "5cd414ae",
+   "execution_count": 11,
+   "id": "a5f2824c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6724557",
+   "id": "fb975d94",
    "metadata": {},
    "source": [
     "GPU parallelization with `vmap` can give a large speed-up, which can easily be 2-3 orders of magnitude."
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df9a0834",
+   "id": "a0cccc7f",
    "metadata": {},
    "source": [
     "### Combining `jit` and `vmap`"
@@ -380,7 +380,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b1ebcf0",
+   "id": "5186402d",
    "metadata": {},
    "source": [
     "Finally, you can also combine using `jit` and `vmap`. For example, you can run multiple batches of many parallel simulations. Each batch can be parallelized with `vmap` and simulating each batch can be compiled with `jit`:"
@@ -388,8 +388,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "e546be6b",
+   "execution_count": 12,
+   "id": "2d6c32ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,8 +398,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "id": "4bad00f6",
+   "execution_count": 13,
+   "id": "03ade1b5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2af2a568",
+   "id": "4b6ec113",
    "metadata": {},
    "source": [
     "That's all you have to know about `jit` and `vmap`! If you have worked through this and the previous tutorials, you should be ready to set up your first network simulations."
@@ -418,12 +418,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2317c987",
+   "id": "2b5e93d1",
    "metadata": {},
    "source": [
     "### Next steps\n",
     "\n",
-    "If you want to learn more, we recommend you to read the [tutorial on building channel and synapse models](https://jaxley.readthedocs.io/en/latest/tutorials/05_channel_and_synapse_models.html) or to read the [tutorial on groups](https://jaxley.readthedocs.io/en/latest/tutorials/06_groups.html), which allow to make your `Jaxley` simulations more elegant and convenient to interact with.\n",
+    "If you want to learn more, we recommend you to read the [tutorial on building channel and synapse models](https://jaxley.readthedocs.io/en/latest/tutorials/05_channel_and_synapse_models.html).\n",
     "\n",
     "Alternatively, you can also directly jump ahead to the [tutorial on training biophysical networks](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html) which will teach you how you can optimize parameters of biophysical models with gradient descent."
    ]
diff --git a/docs/tutorials/05_channel_and_synapse_models.ipynb b/docs/tutorials/05_channel_and_synapse_models.ipynb
index 04e1597b..1ce91d40 100644
--- a/docs/tutorials/05_channel_and_synapse_models.ipynb
+++ b/docs/tutorials/05_channel_and_synapse_models.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6c3cb958",
+   "id": "4ce0e4e0",
    "metadata": {},
    "source": [
     "# Building ion channel models\n",
@@ -17,7 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fb959169",
+   "id": "ed56e35e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a4a2d60",
+   "id": "3c2b7ae6",
    "metadata": {},
    "source": [
     "First, we define a cell as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html):"
@@ -45,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3180d2e9",
+   "id": "7ab6b367",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cdc8a92",
+   "id": "f43fd8a6",
    "metadata": {},
    "source": [
     "You have also already learned how to insert preconfigured channels into `Jaxley` models:\n",
@@ -71,19 +71,17 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30dd0c50",
+   "id": "84c00849",
    "metadata": {},
    "source": [
     "### Your own channel\n",
-    "Below is how you can define your own channel. We will go into detail about individual parts of the code in the next couple of cells.\n",
-    "\n",
-    "Note that a channel needs to have the functions `update_states` and `compute_currents` and `init_states` with all input arguments shown below. "
+    "Below is how you can define your own channel. We will go into detail about individual parts of the code in the next couple of cells."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "ca8fd1ec",
+   "id": "25b24a7f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80b1c993",
+   "id": "f91e4492",
    "metadata": {},
    "source": [
     "Let's look at each part of this in detail. \n",
@@ -158,7 +156,7 @@
     "    def update_states(self, states, dt, v, params):\n",
     "```\n",
     "\n",
-    "The inputs `states` to the `update_states` method is a dictionary which contains all states that are updated (including states of other channels). `v` is a `jnp.ndarray` which contains the voltage of a single compartment (shape `()`). Let's get the state of the potassium channel which we are building here:\n",
+    "Every channel you define must have an `update_states()` method which takes exactly these five arguments (self, states, dt, v, params). The inputs `states` to the `update_states` method is a dictionary which contains all states that are updated (including states of other channels). `v` is a `jnp.ndarray` which contains the voltage of a single compartment (shape `()`). Let's get the state of the potassium channel which we are building here:\n",
     "```python\n",
     "ns = states[\"n_new\"]\n",
     "```\n",
@@ -189,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58878344",
+   "id": "22aa1164",
    "metadata": {},
    "source": [
     "Alright, done! We can now insert this channel into any `jx.Module` such as our cell:"
@@ -198,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "28de17b8",
+   "id": "e1661c2b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "15c389b4",
+   "id": "9e2e9f35",
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ceccb792",
+   "id": "8ed8274c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,12 +240,12 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3714425d",
+   "id": "666d2898",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -266,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd3ffd3d",
+   "id": "aae612b2",
    "metadata": {},
    "source": [
     "### Your own synapse\n",
@@ -280,8 +278,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "919b48fe",
+   "execution_count": 8,
+   "id": "52e39f0a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +310,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe239e53",
+   "id": "2fa83b21",
    "metadata": {},
    "source": [
     "As you can see above, synapses follow closely how channels are defined. The main difference is that the `compute_current` method takes two voltages: the pre-synaptic voltage (a `jnp.ndarray` of shape `()`) and the post-synaptic voltage (a `jnp.ndarray` of shape `()`)."
@@ -320,8 +318,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "84725c6d",
+   "execution_count": 9,
+   "id": "df10f6e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -330,8 +328,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "fdc74265",
+   "execution_count": 10,
+   "id": "aa1ef410",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,8 +342,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "f0e4d172",
+   "execution_count": 11,
+   "id": "d6fe1eef",
    "metadata": {},
    "outputs": [
     {
@@ -367,8 +365,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "5cd518f9",
+   "execution_count": 12,
+   "id": "3d06f3ea",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -377,13 +375,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "a6e4b672",
+   "execution_count": 13,
+   "id": "34273223",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -402,14 +400,12 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71fa5886",
+   "id": "b527efa6",
    "metadata": {},
    "source": [
     "That's it! You are now ready to build your own custom simulations and equip them with channel and synapse models!\n",
     "\n",
-    "This tutorial does not have an immediate follow-up tutorial. You could read the [tutorial on groups](https://jaxley.readthedocs.io/en/latest/tutorials/06_groups.html), which allow to make your `Jaxley` simulations more elegant and convenient to interact with.\n",
-    "\n",
-    "Alternatively, you can also directly jump ahead to the [tutorial on training biophysical networks](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html) which will teach you how you can optimize parameters of biophysical models with gradient descent."
+    "This tutorial does not have an immediate follow-up tutorial. If you have not done so already, you can check out our [tutorial on training biophysical networks](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html) which will teach you how you can optimize parameters of biophysical models with gradient descent."
    ]
   }
  ],
diff --git a/docs/tutorials/07_gradient_descent.ipynb b/docs/tutorials/07_gradient_descent.ipynb
index 710bb4bf..3ca15a55 100644
--- a/docs/tutorials/07_gradient_descent.ipynb
+++ b/docs/tutorials/07_gradient_descent.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c74e729d",
+   "id": "72e2e6e5",
    "metadata": {},
    "source": [
     "# Training biophysical models\n",
@@ -29,15 +29,17 @@
     "parameters = net.get_parameters()\n",
     "\n",
     "# Define parameter transform and apply it to the parameters.\n",
-    "transform = jx.ParamTransform([{\"IonotropicSynapse_gS\": jt.SigmoidTransform(0.0,1.0)},\n",
-    "                               {\"HH_gNa\":jt.SigmoidTransform(0.0,1,0)}])\n",
+    "transform = jx.ParamTransform([\n",
+    "    {\"IonotropicSynapse_gS\": jt.SigmoidTransform(0.0, 1.0)},\n",
+    "    {\"HH_gNa\":jt.SigmoidTransform(0.0, 1, 0)}\n",
+    "])\n",
     "\n",
     "opt_params = transform.inverse(parameters)\n",
     "\n",
     "# Define simulation and batch it across stimuli.\n",
     "def simulate(params, datapoint):\n",
     "    current = jx.datapoint_to_step_currents(i_delay=1.0, i_dur=1.0, i_amps=datapoint, dt=0.025, t_max=5.0)\n",
-    "    data_stimuli = net.cell(0).branch(0).comp(0).data_stimulate(current, None\n",
+    "    data_stimuli = net.cell(0).branch(0).comp(0).data_stimulate(current, None)\n",
     "    return jx.integrate(net, params=params, data_stimuli=data_stimuli, checkpoint_inds=[20, 20])\n",
     "\n",
     "batch_simulate = vmap(simulate, in_axes=(None, 0))\n",
@@ -75,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "81f563ea",
+   "id": "26925a49",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +99,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "48c64bd2",
+   "id": "e14c9b4d",
    "metadata": {},
    "source": [
     "First, we define a network as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html):"
@@ -106,7 +108,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "c78aece7",
+   "id": "dd7f1512",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +133,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e18d91b9",
+   "id": "b8b2f245",
    "metadata": {},
    "source": [
     "This network consists of three neurons arranged in two layers:"
@@ -140,12 +142,12 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "4a737638",
+   "id": "c5bbdba2",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 300x200 with 1 Axes>"
       ]
@@ -163,7 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "048868e0",
+   "id": "5c293af6",
    "metadata": {},
    "source": [
     "We consider the last neuron as the output neuron and record the voltage from there:"
@@ -172,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "85120b89",
+   "id": "0b1c9fe6",
    "metadata": {},
    "outputs": [
     {
@@ -194,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "635784ce",
+   "id": "ff46ddb8",
    "metadata": {},
    "source": [
     "### Defining a dataset"
@@ -202,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c802d08",
+   "id": "d35d24d2",
    "metadata": {},
    "source": [
     "We will train this biophysical network on a classification task. The inputs will be values and the label is binary:"
@@ -211,7 +213,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "cb7c67c1",
+   "id": "d9bed01a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -222,12 +224,12 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e2464398",
+   "id": "cc37d441",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 300x200 with 1 Axes>"
       ]
@@ -245,7 +247,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d772bde2",
+   "id": "6d65174f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ef7eaa8",
+   "id": "daeb0546",
    "metadata": {},
    "source": [
     "### Defining trainable parameters"
@@ -263,7 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ace5389b",
+   "id": "76dfe499",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,7 +274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4448c2b",
+   "id": "fafa89b6",
    "metadata": {},
    "source": [
     "This follows the same API as `.set()` seen in the previous tutorial. If you want to use a single parameter for all `radius`es in the entire network, do:"
@@ -281,7 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "435162e9",
+   "id": "14be84b7",
    "metadata": {},
    "outputs": [
     {
@@ -298,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3a59b7d7",
+   "id": "45f812a2",
    "metadata": {},
    "source": [
     "We can also define parameters for individual compartments. To do this, use the `\"all\"` key. The following defines a separate parameter the sodium conductance for every compartment in the entire network:"
@@ -307,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "82ade63b",
+   "id": "bf97bc41",
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +326,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175e2d5b",
+   "id": "c4bb4fe8",
    "metadata": {},
    "source": [
     "### Making synaptic parameters trainable"
@@ -332,7 +334,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "120908e5",
+   "id": "01ff8f90",
    "metadata": {},
    "source": [
     "Synaptic parameters can be made trainable in the exact same way. To use a single parameter for all syanptic conductances in the entire network, do\n",
@@ -343,7 +345,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4d63fece",
+   "id": "1eb40566",
    "metadata": {},
    "source": [
     "Here, we use a different syanptic conductance for all syanpses. This can be done as follows:"
@@ -352,7 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "fe852cdc",
+   "id": "30d8762d",
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +371,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4df55fc9",
+   "id": "b01533a8",
    "metadata": {},
    "source": [
     "### Running the simulation"
@@ -377,7 +379,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d98b635",
+   "id": "0c0810e5",
    "metadata": {},
    "source": [
     "Once all parameters are defined, you have to use `.get_parameters()` to obtain all trainable parameters. This is also the time to check how many trainable parameters your network has:"
@@ -386,7 +388,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d4e5a301",
+   "id": "2baa41e2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +397,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c38f930e",
+   "id": "627f55e8",
    "metadata": {},
    "source": [
     "You can now run the simulation with the trainable parameters by passing them to the `jx.integrate` function."
@@ -404,7 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "0a2b06a2",
+   "id": "37e08c18",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,7 +415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd6a7f19",
+   "id": "a5385e0f",
    "metadata": {},
    "source": [
     "### Stimulating the network\n",
@@ -424,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "c6746785",
+   "id": "f143c83f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,7 +444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7e7c2b7",
+   "id": "bc2ae28b",
    "metadata": {},
    "source": [
     "We can also inspect some traces:"
@@ -451,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "7047b8eb",
+   "id": "8efb32da",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,12 +463,12 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "097d217a",
+   "id": "040d5db9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
        "<Figure size 400x200 with 1 Axes>"
       ]
@@ -482,7 +484,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "27df7c1a",
+   "id": "1eced80c",
    "metadata": {},
    "source": [
     "### Defining a loss function"
@@ -490,7 +492,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "626acc70",
+   "id": "b5b8aa70",
    "metadata": {},
    "source": [
     "Let us define a loss function to be optimized:"
@@ -499,7 +501,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "7e327bc1",
+   "id": "4eb4742c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7aa4f126",
+   "id": "cbd5421d",
    "metadata": {},
    "source": [
     "And we can use `JAX`'s inbuilt functions to take the gradient through the entire ODE:"
@@ -522,18 +524,17 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "f1740670",
+   "id": "39d962fc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#jitted_grad = jit(value_and_grad(loss, argnums=0))\n",
-    "jitted_grad = (value_and_grad(loss, argnums=0))"
+    "jitted_grad = jit(value_and_grad(loss, argnums=0))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "46556654",
+   "id": "c3ca8223",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,7 +543,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28103af1",
+   "id": "fed46145",
    "metadata": {},
    "source": [
     "### Defining parameter transformations"
@@ -550,7 +551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc293158",
+   "id": "ac447b19",
    "metadata": {},
    "source": [
     "Before training, however, we will enforce for all parameters to be within a prespecified range (such that, e.g., conductances can not become negative)"
@@ -559,7 +560,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "d4a5ee40",
+   "id": "c457f6ba",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +570,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "cecd6e7c",
+   "id": "4a89b640",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,18 +594,18 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "463978d1",
+   "id": "b9643fb4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "transform = jx.ParamTransform([{\"radius\": jt.SigmoidTransform(0.1,5.0)},\n",
-    "                               {\"Leak_gLeak\":jt.SigmoidTransform(1e-5,1e-3)},\n",
-    "                               {\"TanhRateSynapse_gS\" : jt.SigmoidTransform(1e-5,1e-2)}])"
+    "transform = jx.ParamTransform([{\"radius\": jt.SigmoidTransform(0.1, 5.0)},\n",
+    "                               {\"Leak_gLeak\":jt.SigmoidTransform(1e-5, 1e-3)},\n",
+    "                               {\"TanhRateSynapse_gS\" : jt.SigmoidTransform(1e-5, 1e-2)}])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f4461374",
+   "id": "2a870570",
    "metadata": {},
    "source": [
     "With these  modify the loss function acocrdingly:"
@@ -613,7 +614,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "383a82f0",
+   "id": "4a9e90a3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -629,7 +630,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "424ccef5",
+   "id": "dbc515ec",
    "metadata": {},
    "source": [
     "### Using checkpointing"
@@ -637,16 +638,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "321c2995",
+   "id": "e6e7104a",
    "metadata": {},
    "source": [
-    "Checkpointing allows to vastly reduce the memory requirements of training biophysical models."
+    "Checkpointing allows to vastly reduce the memory requirements of training biophysical models (see also [JAX's full tutorial on checkpointing](https://jax.readthedocs.io/en/latest/gradient-checkpointing.html))."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "cdb1647b",
+   "id": "21e5ec64",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +661,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf0a9fe2",
+   "id": "504c5480",
    "metadata": {},
    "source": [
     "To enable checkpointing, we have to modify the `simulate` function appropriately and use\n",
@@ -673,7 +674,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "cae4a2c1",
+   "id": "0b5fa35a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -704,13 +705,12 @@
     "    losses = jnp.abs(predictions - labels)  # Mean absolute error loss.\n",
     "    return jnp.mean(losses)  # Average across the batch.\n",
     "\n",
-    "#jitted_grad = jit(value_and_grad(loss, argnums=0))\n",
-    "jitted_grad = (value_and_grad(loss, argnums=0))"
+    "jitted_grad = jit(value_and_grad(loss, argnums=0))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "eff6bc50",
+   "id": "7fcb1c1a",
    "metadata": {},
    "source": [
     "### Training\n",
@@ -721,7 +721,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "05b6dbfd",
+   "id": "efeb1df2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,7 +731,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "4f43969c",
+   "id": "cf9a48cc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,16 +742,24 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5aca7b6c",
+   "id": "c81e6319",
    "metadata": {},
    "source": [
     "### Writing a dataloader"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "c6932be2",
+   "metadata": {},
+   "source": [
+    "Below, we just write our own (very simple) dataloader. Alternatively, you could use the dataloader from any deep learning library such as pytorch or tensorflow:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "5f8ecd6b",
+   "id": "66c48f06",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -784,22 +792,9 @@
     "            yield self.inputs[start:end], self.labels[start:end]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "b416c56b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch_size = 4\n",
-    "\n",
-    "dataloader = Dataset(inputs, labels)\n",
-    "dataloader = dataloader.shuffle(seed=1).batch(batch_size)"
-   ]
-  },
   {
    "cell_type": "markdown",
-   "id": "8786a19b",
+   "id": "ab4fb02f",
    "metadata": {},
    "source": [
     "### Training loop"
@@ -807,24 +802,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "712276fe",
+   "execution_count": 29,
+   "id": "826b78e8",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 0, loss 25.09776566535514\n"
+      "epoch 0, loss 25.097769115066576\n",
+      "epoch 1, loss 21.143421957090222\n",
+      "epoch 2, loss 15.152694619943718\n",
+      "epoch 3, loss 8.959925803536182\n",
+      "epoch 4, loss 6.721850849093422\n",
+      "epoch 5, loss 6.207057397100044\n",
+      "epoch 6, loss 6.136331377206082\n",
+      "epoch 7, loss 6.102978172393111\n",
+      "epoch 8, loss 6.051937035793608\n",
+      "epoch 9, loss 6.105838843977726\n"
      ]
     }
    ],
    "source": [
+    "batch_size = 4\n",
+    "\n",
     "for epoch in range(10):\n",
     "    epoch_loss = 0.0\n",
+    "    \n",
+    "    # Our simple dummy dataloader must be re-initialized at every epoch.\n",
+    "    dataloader = Dataset(inputs, labels)\n",
+    "    dataloader = dataloader.shuffle(seed=epoch).batch(batch_size)\n",
+    "\n",
     "    for batch_ind, batch in enumerate(dataloader):\n",
-    "        current_batch = batch[0].numpy()\n",
-    "        label_batch = batch[1].numpy()\n",
+    "        current_batch, label_batch = batch\n",
     "        loss_val, gradient = jitted_grad(opt_params, current_batch, label_batch)\n",
     "        updates, opt_state = optimizer.update(gradient, opt_state)\n",
     "        opt_params = optax.apply_updates(opt_params, updates)\n",
@@ -837,8 +847,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
-   "id": "f6743737",
+   "execution_count": 30,
+   "id": "1f2ee1bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -848,13 +858,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
-   "id": "93d4a091",
+   "execution_count": 31,
+   "id": "4133c86c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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JqGV27xQA6q+j7dy5Ex9//LHpdqeLFy+ioqLC1pd0Cinb87Fmr3kxAwCjANbsLUDK9nzHJEZErbKpoJ07dw6RkZGYMGEC5syZg19//RUAsHjxYjz//POyJmhPNXVGvL+vwGLM+/sKUFPn2h0fRO7KpoI2d+5cDB06FFevXkWnTjcHmf7ud78zu43J1XyYdbbJmdmtjKI+joicj02dAvv27cP333/fZDLHHj164JdffpElMUc4V1olaxwR2ZdNZ2hGoxEGg6HJ9gsXLsDX17fNSTlKaICPrHFEZF82FbQHH3wQK1asMH2tUChQUVGB5ORkjBkzRq7c7G5KbA+0NjJDqaiPIyLnY1NBW7p0KQ4cOICIiAhUV1fj8ccfN33cXLx4sdw52o23pxIzRzRdb7SxmSPC4O3p0FmXiKgFNo9Dq6urw+bNm3Hs2DFUVFQgKioKf/rTn8w6CZwRx6ERObe2jEOzuqDV1taib9+++Oqrr9CvXz+r3swZSP1m1dQZ8WHWWdOdAlNie/DMjMgO2lLQrO7l9PLyQnV1y9PruAtvTyVmjOjp6DSIyAo2nXLMmTMHixcvRl1dndz5EBHZzKZxaP/973+RmZmJb7/9FpGRkejcubPZ89ZMwU1EJBebCpq/vz9+//vfy50LEVGbWFXQjEYjlixZgv/973+oqanB/fffj9dee83pezaJqGOw6hraokWL8PLLL+O2225D9+7dsXLlSsyZM6e9ciMisopVBW3Dhg1455138M033yA9PR1ffvklPvroI5efdpuI3INVBa2wsNDs1qa4uDgoFApcvHhR9sQczWAUyDpzBduO/oKsM1dgaG0aDiJyOKuuodXV1UGtNl88xMvLC7W1tbIm5WgZeUVY8GW+2XJ2Oo0ayeMiuEgKkROzqqAJITBt2jSoVCrTturqasyaNcts6IYrD9vIyCvC7I1HcOv5WHFZNWZvPMKVn4icmFUFLSEhocm2yZMny5aMoxmMAgu+zG9SzACYti34Mh8PRGi5YAqRE7KqoK1bt6698nAK2QWlFldNB4CismpkF5QiNryrnbIiIql4t3UjxXpp96hKjSMi+2JBa6S04oascURkXyxojQR09m49yIo4IrIvFrRGtBppt3BJjSMi+2JBayQ6LAA6jdpijE6jRnRYgJ0yIiJrsKA14qFUIHlcBFoakKEAkDwugkM2iJwUC9otRg/Q4d3JUU3O1HQaNQfVEjk5m+ZDc3ejB+jwQIQW2QWluFRejW6+9R8zeWZG5NxY0FrgoVRw8CyRi+FHTiJyG05R0FatWoUePXpArVYjJiYG2dnZFuO3bNmCvn37Qq1WIzIyEtu3b7dTpkTkzBxe0DZv3ozExEQkJyfjyJEjGDhwIOLj43Hp0qVm47///ns89thjmDFjBnJzc/HII4/gkUceQV5enp0zJyJnY/PK6XKJiYnBPffcg7fffhtA/boFISEheOaZZzBv3rwm8RMnTkRlZSW++uor07Zhw4Zh0KBBWL16davvJ3URU4NRsFOAyAHsutCwnGpqapCTk4OkpCTTNqVSibi4OGRlZTW7T1ZWFhITE822xcfHIz09vdn4Gzdu4MaNm/de6vX6VvPiBI9ErsmhHzkvX74Mg8GAoKAgs+1BQUEoLi5udp/i4mKr4lNSUqDRaEyPkJAQizk1TPB46zRCDRM8ZuQVtdYsInIQh19Da29JSUkoKyszPc6fP99irNQJHrm+AJFzcuhHzsDAQHh4eKCkpMRse0lJCbRabbP7aLVaq+JVKpXZlOGWtDbBowAneCRyZg49Q/P29saQIUOQmZlp2mY0GpGZmYnY2Nhm94mNjTWLB4AdO3a0GG+NS+XSJm6UGkdE9uXwOwUSExORkJCAoUOHIjo6GitWrEBlZSWmT58OAJg6dSq6d++OlJQUAMDcuXMxcuRILFu2DGPHjsWmTZtw+PBhvPfee23OpZuv5Zk2rI0jIvtyeEGbOHEifv31V8yfPx/FxcUYNGgQMjIyTBf+CwsLoVTePJEcPnw4/vOf/+CVV17Byy+/jN69eyM9PR0DBgxocy4N0wdZ+tjJ6YOInJfDx6HZW2tjXFK252PN3oIW9//z/4UhaUxEe6ZI1KG1ZRya2/dyWsNgFPjimOVhGV8cK2IvJ5GTYkFrxJpl7IjI+bCgNcJeTiLXxoLWSGBnaePVpMYRkX2xoDUm9d5z3qNO5JRY0Bq5LHEBYalxRGRfLGiNcGAtkWtjQWtkSGgXtDblmVJRH0dEzocFrZGcc1fR2hAzo6iPIyLnw4LWCIdtELk2FrRGAm+TOGxDYhwR2RcLWmNS72jinU9ETokFrZHLlRKHbUiMIyL7YkFrhMM2iFwbC1ojDfOhtTRyQwHOh0bkzFjQGvFQKpA8rn6us1uLWsPXyeMiuD4nkZNiQbvF6AE6vDs5ClqN+cdKrUaNdydHcV1OIifm8Cm4ndHoATo8EKHlyulELqbDFbSGGcelrKDe/3Yv9L/dCwBQWVHernkRUb2G301bVgfocAWtvLy+MLW2gjoROVZ5eTk0Go1V+3S4RVKMRiMuXrwIX19fKBSWP0Lq9XqEhITg/PnzVi/W4KzYJufnbu0BrGuTEALl5eUIDg42W/FNig53hqZUKnHHHXdYtY+fn5/b/GA1YJucn7u1B5DeJmvPzBqwl5OI3AYLGhG5DRY0C1QqFZKTk6FSuc/sGmyT83O39gD2a1OH6xQgIvfFMzQichssaETkNljQiMhtsKARkdvoUAVt1apV6NGjB9RqNWJiYpCdnW0xfsuWLejbty/UajUiIyOxfft2s+eFEJg/fz50Oh06deqEuLg4/Pjjj+3ZhCasadP777+PESNGoEuXLujSpQvi4uKaxE+bNg0KhcLsMXr06PZuhhlr2pSWltYkX7XafKYUVztO9913X5M2KRQKjB071hTjyOO0d+9ejBs3DsHBwVAoFEhPT291nz179iAqKgoqlQq9evVCWlpakxhrfz+bJTqITZs2CW9vb7F27Vpx4sQJMXPmTOHv7y9KSkqajT9w4IDw8PAQb7zxhsjPzxevvPKK8PLyEsePHzfFpKamCo1GI9LT08WxY8fE+PHjRVhYmLh+/bpTtunxxx8Xq1atErm5ueLkyZNi2rRpQqPRiAsXLphiEhISxOjRo0VRUZHpUVpaapf2CGF9m9atWyf8/PzM8i0uLjaLcbXjdOXKFbP25OXlCQ8PD7Fu3TpTjCOP0/bt28Xf//53sXXrVgFAfP755xbjf/75Z+Hj4yMSExNFfn6+eOutt4SHh4fIyMgwxVj7PWpJhylo0dHRYs6cOaavDQaDCA4OFikpKc3G//GPfxRjx4412xYTEyP+/Oc/CyGEMBqNQqvViiVLlpiev3btmlCpVOLjjz9uhxY0ZW2bblVXVyd8fX3F+vXrTdsSEhLEhAkT5E5VMmvbtG7dOqHRaFp8PXc4Tm+++abw9fUVFRUVpm2OPk4NpBS0F198UfTv399s28SJE0V8fLzp67Z+jxp0iI+cNTU1yMnJQVxcnGmbUqlEXFwcsrKymt0nKyvLLB4A4uPjTfEFBQUoLi42i9FoNIiJiWnxNeVkS5tuVVVVhdraWgQEmE8pvmfPHnTr1g19+vTB7NmzceXKFVlzb4mtbaqoqEBoaChCQkIwYcIEnDhxwvScOxynDz74AJMmTULnzp3NtjvqOFmrtd8lOb5Hpv3anq7zu3z5MgwGA4KCgsy2BwUFobi4uNl9iouLLcY3/GvNa8rJljbd6qWXXkJwcLDZD9Lo0aOxYcMGZGZmYvHixfjuu+/w0EMPwWAwyJp/c2xpU58+fbB27Vps27YNGzduhNFoxPDhw3HhwgUArn+csrOzkZeXhyeffNJsuyOPk7Va+l3S6/W4fv26LD/LDTrcbBtULzU1FZs2bcKePXvMLqJPmjTJ9P/IyEjcfffdCA8Px549ezBq1ChHpGpRbGwsYmNjTV8PHz4c/fr1w5o1a7Bw4UIHZiaPDz74AJGRkYiOjjbb7mrHyV46xBlaYGAgPDw8UFJSYra9pKQEWq222X20Wq3F+IZ/rXlNOdnSpgZLly5Famoqvv32W9x9990WY3v27InAwED89NNPbc65NW1pUwMvLy8MHjzYlK8rH6fKykps2rQJM2bMaPV97HmcrNXS75Kfnx86deoky3Fv0CEKmre3N4YMGYLMzEzTNqPRiMzMTLO/7o3FxsaaxQPAjh07TPFhYWHQarVmMXq9HocOHWrxNeVkS5sA4I033sDChQuRkZGBoUOHtvo+Fy5cwJUrV6DTtf/iMLa2qTGDwYDjx4+b8nXV4wTUDxu6ceMGJk+e3Or72PM4Wau13yU5jruJVV0ILmzTpk1CpVKJtLQ0kZ+fL5566inh7+9v6uKfMmWKmDdvnin+wIEDwtPTUyxdulScPHlSJCcnNztsw9/fX2zbtk388MMPYsKECXYfDmBNm1JTU4W3t7f49NNPzbr7y8vLhRBClJeXi+eff15kZWWJgoICsXPnThEVFSV69+4tqqurnbJNCxYsEN988404c+aMyMnJEZMmTRJqtVqcOHHCrN2udJwa/OY3vxETJ05sst3Rx6m8vFzk5uaK3NxcAUAsX75c5ObminPnzgkhhJg3b56YMmWKKb5h2MYLL7wgTp48KVatWtXssA1L3yOpOkxBE0KIt956S9x5553C29tbREdHi4MHD5qeGzlypEhISDCL/+STT8Rdd90lvL29Rf/+/cXXX39t9rzRaBSvvvqqCAoKEiqVSowaNUqcPn3aHk0xsaZNoaGhAkCTR3JyshBCiKqqKvHggw+K22+/XXh5eYnQ0FAxc+ZMq3+o7Nmm5557zhQbFBQkxowZI44cOWL2eq52nIQQ4tSpUwKA+Pbbb5u8lqOP0+7du5v9OWpoQ0JCghg5cmSTfQYNGiS8vb1Fz549zcbUNbD0PZKK0wcRkdvoENfQiKhjYEEjIrfBgkZEboMFjYjcBgsaEbkNFjQichssaETkNljQyGWlpaXB39+/za8jddZVcn4saORQ06ZNwyOPPOLoNMhNsKARkdtgQSOntXz5ckRGRqJz584ICQnB008/jYqKiiZx6enp6N27N9RqNeLj43H+/Hmz57dt24aoqCio1Wr07NkTCxYsQF1dnb2aQXbEgkZOS6lUYuXKlThx4gTWr1+PXbt24cUXXzSLqaqqwqJFi7BhwwYcOHAA165dM5v8cN++fZg6dSrmzp2L/Px8rFmzBmlpaVi0aJG9m0P2YPXt7EQysmaxjy1btoiuXbuavl63bp0AYDYrw8mTJwUAcejQISGEEKNGjRKvv/662et8+OGHQqfTmb6GhIU+yDVwCm5yWjt37kRKSgpOnToFvV6Puro6VFdXo6qqCj4+PgAAT09P3HPPPaZ9+vbtC39/f5w8eRLR0dE4duwYDhw4YHZGZjAYmrwOuQcWNHJKZ8+excMPP4zZs2dj0aJFCAgIwP79+zFjxgzU1NRILkQVFRVYsGABHn300SbP3bogMbk+FjRySjk5OTAajVi2bBmUyvpLvZ988kmTuLq6Ohw+fNi0iMjp06dx7do19OvXDwAQFRWF06dPo1evXvZLnhyGBY0crqysDEePHjXbFhgYiNraWrz11lsYN24cDhw4gNWrVzfZ18vLC8888wxWrlwJT09P/OUvf8GwYcNMBW7+/Pl4+OGHceedd+IPf/gDlEoljh07hry8PPzzn/+0R/PInhx9EY86toSEhGanc54xY4ZYvny50Ol0olOnTiI+Pl5s2LBBABBXr14VQtxcNf2zzz4TPXv2FCqVSsTFxZnmtm+QkZEhhg8fLjp16iT8/PxEdHS0eO+990zPg50CboNTcBOR2+A4NCJyGyxoROQ2WNCIyG2woBGR22BBIyK3wYJGRG6DBY2I3AYLGhG5DRY0InIbLGhE5DZY0IjIbbCgEZHb+H8AW6K0yh/w6gAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 300x200 with 1 Axes>"
       ]
@@ -872,7 +882,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "baa6e15b",
+   "id": "a4ff162c",
    "metadata": {},
    "source": [
     "Indeed, the loss goes down and the network successfully classifies the patterns."
@@ -880,7 +890,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35008f21",
+   "id": "c96b2892",
    "metadata": {},
    "source": [
     "### Summary"
@@ -888,7 +898,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47f7b496",
+   "id": "d7eee2e7",
    "metadata": {},
    "source": [
     "Puh, this was a pretty dense tutorial with a lot of material. You should have learned how to:\n",
@@ -902,10 +912,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43a60661",
+   "id": "0e16c465",
    "metadata": {},
    "source": [
-    "This was one of the last tutorials of the `Jaxley` toolbox. If anything is still unclear please create a [discussion](https://github.com/jaxleyverse/jaxley/discussions). If you find any bugs, please open an [issue](https://github.com/jaxleyverse/jaxley/issues). Happy coding!"
+    "This was the last \"basic\" tutorial of the `Jaxley` toolbox. If you want to learn more, check out our [Advanced Tutorials](https://jaxley.readthedocs.io/en/latest/advanced_tutorials.html). If anything is still unclear please create a [discussion](https://github.com/jaxleyverse/jaxley/discussions). If you find any bugs, please open an [issue](https://github.com/jaxleyverse/jaxley/issues). Happy coding!"
    ]
   }
  ],